Дисертації: "Text document classification"

1

Mondal, Abhro Jyoti. "Document Classification using Characteristic Signatures." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511793852923472.

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2

Sendur, Zeynel. "Text Document Categorization by Machine Learning." Scholarly Repository, 2008. http://scholarlyrepository.miami.edu/oa_theses/209.

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Because of the explosion of digital and online text information, automatic organization of documents has become a very important research area. There are mainly two machine learning approaches to enhance the organization task of the digital documents. One of them is the supervised approach, where pre-defined category labels are assigned to documents based on the likelihood suggested by a training set of labeled documents; and the other one is the unsupervised approach, where there is no need for human intervention or labeled documents at any point in the whole process. In this thesis, we concentrate on the supervised learning task which deals with document classification. One of the most important tasks of information retrieval is to induce classifiers capable of categorizing text documents. The same document can belong to two or more categories and this situation is referred by the term multi-label classification. Multi-label classification domains have been encountered in diverse fields. Most of the existing machine learning techniques which are in multi-label classification domains are extremely expensive since the documents are characterized by an extremely large number of features. In this thesis, we are trying to reduce these computational costs by applying different types of algorithms to the documents which are characterized by large number of features. Another important thing that we deal in this thesis is to have the highest possible accuracy when we have the high computational performance on text document categorization.

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3

Blein, Florent. "Automatic Document Classification Applied to Swedish News." Thesis, Linköping University, Department of Computer and Information Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-3065.

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The first part of this paper presents briefly the ELIN[1] system, an electronic newspaper project. ELIN is a framework that stores news and displays them to the end-user. Such news are formatted using the xml[2] format. The project partner Corren[3] provided ELIN with xml articles, however the format used was not the same. My first task has been to develop a software that converts the news from one xml format (Corren) to another (ELIN).

The second and main part addresses the problem of automatic document classification and tries to find a solution for a specific issue. The goal is to automatically classify news articles from a Swedish newspaper company (Corren) into the IPTC[4] news categories.

This work has been carried out by implementing several classification algorithms, testing them and comparing their accuracy with existing software. The training and test documents were 3 weeks of the Corren newspaper that had to be classified into 2 categories.

The last tests were run with only one algorithm (Naïve Bayes) over a larger amount of data (7, then 10 weeks) and categories (12) to simulate a more real environment.

The results show that the Naïve Bayes algorithm, although the oldest, was the most accurate in this particular case. An issue raised by the results is that feature selection improves speed but can seldom reduce accuracy by removing too many features.

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4

Anne, Chaitanya. "Advanced Text Analytics and Machine Learning Approach for Document Classification." ScholarWorks@UNO, 2017. http://scholarworks.uno.edu/td/2292.

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Text classification is used in information extraction and retrieval from a given text, and text classification has been considered as an important step to manage a vast number of records given in digital form that is far-reaching and expanding. This thesis addresses patent document classification problem into fifteen different categories or classes, where some classes overlap with other classes for practical reasons. For the development of the classification model using machine learning techniques, useful features have been extracted from the given documents. The features are used to classify patent document as well as to generate useful tag-words. The overall objective of this work is to systematize NASA’s patent management, by developing a set of automated tools that can assist NASA to manage and market its portfolio of intellectual properties (IP), and to enable easier discovery of relevant IP by users. We have identified an array of methods that can be applied such as k-Nearest Neighbors (kNN), two variations of the Support Vector Machine (SVM) algorithms, and two tree based classification algorithms: Random Forest and J48. The major research steps in this work consist of filtering techniques for variable selection, information gain and feature correlation analysis, and training and testing potential models using effective classifiers. Further, the obstacles associated with the imbalanced data were mitigated by adding synthetic data wherever appropriate, which resulted in a superior SVM classifier based model.

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5

Alsaad, Amal. "Enhanced root extraction and document classification algorithm for Arabic text." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13510.

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Many text extraction and classification systems have been developed for English and other international languages; most of the languages are based on Roman letters. However, Arabic language is one of the difficult languages which have special rules and morphology. Not many systems have been developed for Arabic text categorization. Arabic language is one of the Semitic languages with morphology that is more complicated than English. Due to its complex morphology, there is a need for pre-processing routines to extract the roots of the words then classify them according to the group of acts or meaning. In this thesis, a system has been developed and tested for text classification. The system is based on two stages, the first is to extract the roots from text and the second is to classify the text according to predefined categories. The linguistic root extraction stage is composed of two main phases. The first phase is to handle removal of affixes including prefixes, suffixes and infixes. Prefixes and suffixes are removed depending on the length of the word, while checking its morphological pattern after each deduction to remove infixes. In the second phase, the root extraction algorithm is formulated to handle weak, defined, eliminated-long-vowel and two-letter geminated words, as there is a substantial great amount of irregular Arabic words in texts. Once the roots are extracted, they are checked against a predefined list of 3800 triliteral and 900 quad literal roots. Series of experiments has been conducted to improve and test the performance of the proposed algorithm. The obtained results revealed that the developed algorithm has better accuracy than the existing stemming algorithm. The second stage is the document classification stage. In this stage two non-parametric classifiers are tested, namely Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The system is trained on 6 categories: culture, economy, international, local, religion and sports. The system is trained on 80% of the available data. From each category, the 10 top frequent terms are selected as features. Testing the classification algorithms has been done on the remaining 20% of the documents. The results of ANN and SVM are compared to the standard method used for text classification, the terms frequency-based method. Results show that ANN and SVM have better accuracy (80-90%) compared to the standard method (60-70%). The proposed method proves the ability to categorize the Arabic text documents into the appropriate categories with a high precision rate.

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6

McElroy, Jonathan David. "Automatic Document Classification in Small Environments." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/682.

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Document classification is used to sort and label documents. This gives users quicker access to relevant data. Users that work with large inflow of documents spend time filing and categorizing them to allow for easier procurement. The Automatic Classification and Document Filing (ACDF) system proposed here is designed to allow users working with files or documents to rely on the system to classify and store them with little manual attention. By using a system built on Hidden Markov Models, the documents in a smaller desktop environment are categorized with better results than the traditional Naive Bayes implementation of classification.

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7

Felhi, Mehdi. "Document image segmentation : content categorization." Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0109/document.

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Dans cette thèse, nous abordons le problème de la segmentation des images de documents en proposant de nouvelles approches pour la détection et la classification de leurs contenus. Dans un premier lieu, nous étudions le problème de l'estimation d'inclinaison des documents numérisées. Le but de ce travail étant de développer une approche automatique en mesure d'estimer l'angle d'inclinaison du texte dans les images de document. Notre méthode est basée sur la méthode Maximum Gradient Difference (MGD), la R-signature et la transformée de Ridgelets. Nous proposons ensuite une approche hybride pour la segmentation des documents. Nous décrivons notre descripteur de trait qui permet de détecter les composantes de texte en se basant sur la squeletisation. La méthode est appliquée pour la segmentation des images de documents numérisés (journaux et magazines) qui contiennent du texte, des lignes et des régions de photos. Le dernier volet de la thèse est consacré à la détection du texte dans les photos et posters. Pour cela, nous proposons un ensemble de descripteurs de texte basés sur les caractéristiques du trait. Notre approche commence par l'extraction et la sélection des candidats de caractères de texte. Deux méthodes ont été établies pour regrouper les caractères d'une même ligne de texte (mot ou phrase) ; l'une consiste à parcourir en profondeur un graphe, l'autre consiste à établir un critère de stabilité d'une région de texte. Enfin, les résultats sont affinés en classant les candidats de texte en régions « texte » et « non-texte » en utilisant une version à noyau du classifieur Support Vector Machine (K-SVM)
In this thesis I discuss the document image segmentation problem and I describe our new approaches for detecting and classifying document contents. First, I discuss our skew angle estimation approach. The aim of this approach is to develop an automatic approach able to estimate, with precision, the skew angle of text in document images. Our method is based on Maximum Gradient Difference (MGD) and R-signature. Then, I describe our second method based on Ridgelet transform.Our second contribution consists in a new hybrid page segmentation approach. I first describe our stroke-based descriptor that allows detecting text and line candidates using the skeleton of the binarized document image. Then, an active contour model is applied to segment the rest of the image into photo and background regions. Finally, text candidates are clustered using mean-shift analysis technique according to their corresponding sizes. The method is applied for segmenting scanned document images (newspapers and magazines) that contain text, lines and photo regions. Finally, I describe our stroke-based text extraction method. Our approach begins by extracting connected components and selecting text character candidates over the CIE LCH color space using the Histogram of Oriented Gradients (HOG) correlation coefficients in order to detect low contrasted regions. The text region candidates are clustered using two different approaches ; a depth first search approach over a graph, and a stable text line criterion. Finally, the resulted regions are refined by classifying the text line candidates into « text» and « non-text » regions using a Kernel Support Vector Machine K-SVM classifier

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8

Wang, Yanbo Justin. "Language-independent pre-processing of large document bases for text classification." Thesis, University of Liverpool, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445960.

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Text classification is a well-known topic in the research of knowledge discovery in databases. Algorithms for text classification generally involve two stages. The first is concerned with identification of textual features (i.e. words andlor phrases) that may be relevant to the classification process. The second is concerned with classification rule mining and categorisation of "unseen" textual data. The first stage is the subject of this thesis and often involves an analysis of text that is both language-specific (and possibly domain-specific), and that may also be computationally costly especially when dealing with large datasets. Existing approaches to this stage are not, therefore, generally applicable to all languages. In this thesis, we examine a number of alternative keyword selection methods and phrase generation strategies, coupled with two potential significant word list construction mechanisms and two final significant word selection mechanisms, to identify such words andlor phrases in a given textual dataset that are expected to serve to distinguish between classes, by simple, language-independent statistical properties. We present experimental results, using common (large) textual datasets presented in two distinct languages, to show that the proposed approaches can produce good performance with respect to both classification accuracy and processing efficiency. In other words, the study presented in this thesis demonstrates the possibility of efficiently solving the traditional text classification problem in a language-independent (also domain-independent) manner.

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9

Wang, Yalin. "Document analysis : table structure understanding and zone content classification /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/6079.

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10

Wei, Zhihua. "The research on chinese text multi-label classification." Thesis, Lyon 2, 2010. http://www.theses.fr/2010LYO20025/document.

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Text Classification (TC) which is an important field in information technology has many valuable applications. When facing the sea of information resources, the objects of TC are more complicated and diversity. The researches in pursuit of effective and practical TC technology are fairly challenging. More and more researchers regard that multi-label TC is more suited for many applications. This thesis analyses the difficulties and problems in multi-label TC and Chinese text representation based on a mass of algorithms for single-label TC and multi-label TC. Aiming at high dimensionality in feature space, sparse distribution in text representation and poor performance of multi-label classifier, this thesis will bring forward corresponding algorithms from different angles.Focusing on the problem of dimensionality “disaster” when Chinese texts are represented by using n-grams, two-step feature selection algorithm is constructed. The method combines filtering rare features within class and selecting discriminative features across classes. Moreover, the proper value of “n”, the strategy of feature weight and the correlation among features are discussed based on variety of experiments. Some useful conclusions are contributed to the research of n-gram representation in Chinese texts.In a view of the disadvantage in Latent Dirichlet Allocation (LDA) model, that is, arbitrarily revising the variable in smooth process, a new strategy for smoothing based on Tolerance Rough Set (TRS) is put forward. It constructs tolerant class in global vocabulary database firstly and then assigns value for out-of-vocabulary (oov) word in each class according to tolerant class.In order to improve performance of multi-label classifier and degrade computing complexity, a new TC method based on LDA model is applied for Chinese text representation. It extracts topics statistically from texts and then texts are represented by using the topic vector. It shows competitive performance both in English and in Chinese corpus.To enhance the performance of classifiers in multi-label TC, a compound classification framework is raised. It partitions the text space by computing the upper approximation and lower approximation. This algorithm decomposes a multi-label TC problem into several single-label TCs and several multi-label TCs which have less labels than original problem. That is, an unknown text should be classified by single-label classifier when it is partitioned into lower approximation space of some class. Otherwise, it should be classified by corresponding multi-label classifier.An application system TJ-MLWC (Tongji Multi-label Web Classifier) was designed. It could call the result from Search Engines directly and classify these results real-time using improved Naïve Bayes classifier. This makes the browse process more conveniently for users. Users could locate the texts interested immediately according to the class information given by TJ-MLWC
La thèse est centrée sur la Classification de texte, domaine en pleine expansion, avec de nombreuses applications actuelles et potentielles. Les apports principaux de la thèse portent sur deux points : Les spécificités du codage et du traitement automatique de la langue chinoise : mots pouvant être composés de un, deux ou trois caractères ; absence de séparation typographique entre les mots ; grand nombre d’ordres possibles entre les mots d’une phrase ; tout ceci aboutissant à des problèmes difficiles d’ambiguïté. La solution du codage en «n-grams »(suite de n=1, ou 2 ou 3 caractères) est particulièrement adaptée à la langue chinoise, car elle est rapide et ne nécessite pas les étapes préalables de reconnaissance des mots à l’aide d’un dictionnaire, ni leur séparation. La classification multi-labels, c'est-à-dire quand chaque individus peut être affecté à une ou plusieurs classes. Dans le cas des textes, on cherche des classes qui correspondent à des thèmes (topics) ; un même texte pouvant être rattaché à un ou plusieurs thème. Cette approche multilabel est plus générale : un même patient peut être atteint de plusieurs pathologies ; une même entreprise peut être active dans plusieurs secteurs industriels ou de services. La thèse analyse ces problèmes et tente de leur apporter des solutions, d’abord pour les classifieurs unilabels, puis multi-labels. Parmi les difficultés, la définition des variables caractérisant les textes, leur grand nombre, le traitement des tableaux creux (beaucoup de zéros dans la matrice croisant les textes et les descripteurs), et les performances relativement mauvaises des classifieurs multi-classes habituels
文本分类是信息科学中一个重要而且富有实际应用价值的研究领域。随着文本分类处理内容日趋复杂化和多元化，分类目标也逐渐多样化，研究有效的、切合实际应用需求的文本分类技术成为一个很有挑战性的任务，对多标签分类的研究应运而生。本文在对大量的单标签和多标签文本分类算法进行分析和研究的基础上，针对文本表示中特征高维问题、数据稀疏问题和多标签分类中分类复杂度高而精度低的问题，从不同的角度尝试运用粗糙集理论加以解决，提出了相应的算法，主要包括：针对n-gram作为中文文本特征时带来的维数灾难问题，提出了两步特征选择的方法，即去除类内稀有特征和类间特征选择相结合的方法，并就n-gram作为特征时的n值选取、特征权重的选择和特征相关性等问题在大规模中文语料库上进行了大量的实验，得出一些有用的结论。针对文本分类中运用高维特征表示文本带来的分类效率低，开销大等问题，提出了基于LDA模型的多标签文本分类算法，利用LDA模型提取的主题作为文本特征，构建高效的分类器。在PT3多标签分类转换方法下，该分类算法在中英文数据集上都表现出很好的效果，与目前公认最好的多标签分类方法效果相当。针对LDA模型现有平滑策略的随意性和武断性的缺点，提出了基于容差粗糙集的LDA语言模型平滑策略。该平滑策略首先在全局词表上构造词的容差类，再根据容差类中词的频率为每类文档的未登录词赋予平滑值。在中英文、平衡和不平衡语料库上的大量实验都表明该平滑方法显著提高了LDA模型的分类性能，在不平衡语料库上的提高尤其明显。针对多标签分类中分类复杂度高而精度低的问题，提出了一种基于可变精度粗糙集的复合多标签文本分类框架，该框架通过可变精度粗糙集方法划分文本特征空间，进而将多标签分类问题分解为若干个两类单标签分类问题和若干个标签数减少了的多标签分类问题。即，当一篇未知文本被划分到某一类文本的下近似区域时，可以直接用简单的单标签文本分类器判断其类别；当未知文本被划分在边界域时，则采用相应区域的多标签分类器进行分类。实验表明，这种分类框架下，分类的精确度和算法效率都有较大的提高。本文还设计和实现了一个基于多标签分类的网页搜索结果可视化系统（MLWC），该系统能够直接调用搜索引擎返回的搜索结果，并采用改进的Naïve Bayes多标签分类算法实现实时的搜索结果分类，使用户可以快速地定位搜索结果中感兴趣的文本。

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11

Tisserant, Guillaume. "Généralisation de données textuelles adaptée à la classification automatique." Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTS231/document.

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La classification de documents textuels est une tâche relativement ancienne. Très tôt, de nombreux documents de différentes natures ont été regroupés dans le but de centraliser la connaissance. Des systèmes de classement et d'indexation ont alors été créés. Ils permettent de trouver facilement des documents en fonction des besoins des lecteurs. Avec la multiplication du nombre de documents et l'apparition de l'informatique puis d'internet, la mise en œuvre de systèmes de classement des textes devient un enjeu crucial. Or, les données textuelles, de nature complexe et riche, sont difficiles à traiter de manière automatique. Dans un tel contexte, cette thèse propose une méthodologie originale pour organiser l'information textuelle de façon à faciliter son accès. Nos approches de classification automatique de textes mais aussi d'extraction d'informations sémantiques permettent de retrouver rapidement et avec pertinence une information recherchée.De manière plus précise, ce manuscrit présente de nouvelles formes de représentation des textes facilitant leur traitement pour des tâches de classification automatique. Une méthode de généralisation partielle des données textuelles (approche GenDesc) s'appuyant sur des critères statistiques et morpho-syntaxiques est proposée. Par ailleurs, cette thèse s'intéresse à la construction de syntagmes et à l'utilisation d'informations sémantiques pour améliorer la représentation des documents. Nous démontrerons à travers de nombreuses expérimentations la pertinence et la généricité de nos propositions qui permettent une amélioration des résultats de classification. Enfin, dans le contexte des réseaux sociaux en fort développement, une méthode de génération automatique de HashTags porteurs de sémantique est proposée. Notre approche s'appuie sur des mesures statistiques, des ressources sémantiques et l'utilisation d'informations syntaxiques. Les HashTags proposés peuvent alors être exploités pour des tâches de recherche d'information à partir de gros volumes de données
We have work for a long time on the classification of text. Early on, many documents of different types were grouped in order to centralize knowledge. Classification and indexing systems were then created. They make it easy to find documents based on readers' needs. With the increasing number of documents and the appearance of computers and the internet, the implementation of text classification systems becomes a critical issue. However, textual data, complex and rich nature, are difficult to treat automatically. In this context, this thesis proposes an original methodology to organize and facilitate the access to textual information. Our automatic classification approache and our semantic information extraction enable us to find quickly a relevant information.Specifically, this manuscript presents new forms of text representation facilitating their processing for automatic classification. A partial generalization of textual data (GenDesc approach) based on statistical and morphosyntactic criteria is proposed. Moreover, this thesis focuses on the phrases construction and on the use of semantic information to improve the representation of documents. We will demonstrate through numerous experiments the relevance and genericity of our proposals improved they improve classification results.Finally, as social networks are in strong development, a method of automatic generation of semantic Hashtags is proposed. Our approach is based on statistical measures, semantic resources and the use of syntactic information. The generated Hashtags can then be exploited for information retrieval tasks from large volumes of data

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12

Mazyad, Ahmad. "Contribution to automatic text classification : metrics and evolutionary algorithms." Thesis, Littoral, 2018. http://www.theses.fr/2018DUNK0487/document.

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Анотація:

Cette thèse porte sur le traitement du langage naturel et l'exploration de texte, à l'intersection de l'apprentissage automatique et de la statistique. Nous nous intéressons plus particulièrement aux schémas de pondération des termes (SPT) dans le contexte de l'apprentissage supervisé et en particulier à la classification de texte. Dans la classification de texte, la tâche de classification multi-étiquettes a suscité beaucoup d'intérêt ces dernières années. La classification multi-étiquettes à partir de données textuelles peut être trouvée dans de nombreuses applications modernes telles que la classification de nouvelles où la tâche est de trouver les catégories auxquelles appartient un article de presse en fonction de son contenu textuel (par exemple, politique, Moyen-Orient, pétrole), la classification du genre musical (par exemple, jazz, pop, oldies, pop traditionnelle) en se basant sur les commentaires des clients, la classification des films (par exemple, action, crime, drame), la classification des produits (par exemple, électronique, ordinateur, accessoires). La plupart des algorithmes d'apprentissage ne conviennent qu'aux problèmes de classification binaire. Par conséquent, les tâches de classification multi-étiquettes sont généralement transformées en plusieurs tâches binaires à label unique. Cependant, cette transformation introduit plusieurs problèmes. Premièrement, les distributions des termes ne sont considérés qu'en matière de la catégorie positive et de la catégorie négative (c'est-à-dire que les informations sur les corrélations entre les termes et les catégories sont perdues). Deuxièmement, il n'envisage aucune dépendance vis-à-vis des étiquettes (c'est-à-dire que les informations sur les corrélations existantes entre les classes sont perdues). Enfin, puisque toutes les catégories sauf une sont regroupées dans une seule catégories (la catégorie négative), les tâches nouvellement créées sont déséquilibrées. Ces informations sont couramment utilisées par les SPT supervisés pour améliorer l'efficacité du système de classification. Ainsi, après avoir présenté le processus de classification de texte multi-étiquettes, et plus particulièrement le SPT, nous effectuons une comparaison empirique de ces méthodes appliquées à la tâche de classification de texte multi-étiquette. Nous constatons que la supériorité des méthodes supervisées sur les méthodes non supervisées n'est toujours pas claire. Nous montrons ensuite que ces méthodes ne sont pas totalement adaptées au problème de la classification multi-étiquettes et qu'elles ignorent beaucoup d'informations statistiques qui pourraient être utilisées pour améliorer les résultats de la classification. Nous proposons donc un nouvel SPT basé sur le gain d'information. Cette nouvelle méthode prend en compte la distribution des termes, non seulement en ce qui concerne la catégorie positive et la catégorie négative, mais également en rapport avec toutes les autres catégories. Enfin, dans le but de trouver des SPT spécialisés qui résolvent également le problème des tâches déséquilibrées, nous avons étudié les avantages de l'utilisation de la programmation génétique pour générer des SPT pour la tâche de classification de texte. Contrairement aux études précédentes, nous générons des formules en combinant des informations statistiques à un niveau microscopique (par exemple, le nombre de documents contenant un terme spécifique) au lieu d'utiliser des SPT complets. De plus, nous utilisons des informations catégoriques telles que (par exemple, le nombre de catégories dans lesquelles un terme apparaît). Des expériences sont effectuées pour mesurer l'impact de ces méthodes sur les performances du modèle. Nous montrons à travers ces expériences que les résultats sont positifs
This thesis deals with natural language processing and text mining, at the intersection of machine learning and statistics. We are particularly interested in Term Weighting Schemes (TWS) in the context of supervised learning and specifically the Text Classification (TC) task. In TC, the multi-label classification task has gained a lot of interest in recent years. Multi-label classification from textual data may be found in many modern applications such as news classification where the task is to find the categories that a newswire story belongs to (e.g., politics, middle east, oil), based on its textual content, music genre classification (e.g., jazz, pop, oldies, traditional pop) based on customer reviews, film classification (e.g. action, crime, drama), product classification (e.g. Electronics, Computers, Accessories). Traditional classification algorithms are generally binary classifiers, and they are not suited for the multi-label classification. The multi-label classification task is, therefore, transformed into multiple single-label binary tasks. However, this transformation introduces several issues. First, terms distributions are only considered in relevance to the positive and the negative categories (i.e., information on the correlations between terms and categories is lost). Second, it fails to consider any label dependency (i.e., information on existing correlations between classes is lost). Finally, since all categories but one are grouped into one category (the negative category), the newly created tasks are imbalanced. This information is commonly used by supervised TWS to improve the effectiveness of the classification system. Hence, after presenting the process of multi-label text classification, and more particularly the TWS, we make an empirical comparison of these methods applied to the multi-label text classification task. We find that the superiority of the supervised methods over the unsupervised methods is still not clear. We show then that these methods are not fully adapted to the multi-label classification problem and they ignore much statistical information that coul be used to improve the classification results. Thus, we propose a new TWS based on information gain. This new method takes into consideration the term distribution, not only regarding the positive and the negative categories but also in relevance to all classes. Finally, aiming at finding specialized TWS that also solve the issue of imbalanced tasks, we studied the benefits of using genetic programming for generating TWS for the text classification task. Unlike previous studies, we generate formulas by combining statistical information at a microscopic level (e.g., the number of documents that contain a specific term) instead of using complete TWS. Furthermore, we make use of categorical information such as (e.g., the number of categories where a term occurs). Experiments are made to measure the impact of these methods on the performance of the model. We show through these experiments that the results are positive

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13

Martinez-Alvarez, Miguel. "Knowledge-enhanced text classification : descriptive modelling and new approaches." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/27205.

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The knowledge available to be exploited by text classification and information retrieval systems has significantly changed, both in nature and quantity, in the last years. Nowadays, there are several sources of information that can potentially improve the classification process, and systems should be able to adapt to incorporate multiple sources of available data in different formats. This fact is specially important in environments where the required information changes rapidly, and its utility may be contingent on timely implementation. For these reasons, the importance of adaptability and flexibility in information systems is rapidly growing. Current systems are usually developed for specific scenarios. As a result, significant engineering effort is needed to adapt them when new knowledge appears or there are changes in the information needs. This research investigates the usage of knowledge within text classification from two different perspectives. On one hand, the application of descriptive approaches for the seamless modelling of text classification, focusing on knowledge integration and complex data representation. The main goal is to achieve a scalable and efficient approach for rapid prototyping for Text Classification that can incorporate different sources and types of knowledge, and to minimise the gap between the mathematical definition and the modelling of a solution. On the other hand, the improvement of different steps of the classification process where knowledge exploitation has traditionally not been applied. In particular, this thesis introduces two classification sub-tasks, namely Semi-Automatic Text Classification (SATC) and Document Performance Prediction (DPP), and several methods to address them. SATC focuses on selecting the documents that are more likely to be wrongly assigned by the system to be manually classified, while automatically labelling the rest. Document performance prediction estimates the classification quality that will be achieved for a document, given a classifier. In addition, we also propose a family of evaluation metrics to measure degrees of misclassification, and an adaptive variation of k-NN.

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14

Al-Nashashibi, May Y. A. "Arabic Language Processing for Text Classification. Contributions to Arabic Root Extraction Techniques, Building An Arabic Corpus, and to Arabic Text Classification Techniques." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/6326.

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The impact and dynamics of Internet-based resources for Arabic-speaking users is increasing in significance, depth and breadth at highest pace than ever, and thus requires updated mechanisms for computational processing of Arabic texts. Arabic is a complex language and as such requires in depth investigation for analysis and improvement of available automatic processing techniques such as root extraction methods or text classification techniques, and for developing text collections that are already labeled, whether with single or multiple labels. This thesis proposes new ideas and methods to improve available automatic processing techniques for Arabic texts. Any automatic processing technique would require data in order to be used and critically reviewed and assessed, and here an attempt to develop a labeled Arabic corpus is also proposed. This thesis is composed of three parts: 1- Arabic corpus development, 2- proposing, improving and implementing root extraction techniques, and 3- proposing and investigating the effect of different pre-processing methods on single-labeled text classification methods for Arabic. This thesis first develops an Arabic corpus that is prepared to be used here for testing root extraction methods as well as single-label text classification techniques. It also enhances a rule-based root extraction method by handling irregular cases (that appear in about 34% of texts). It proposes and implements two expanded algorithms as well as an adjustment for a weight-based method. It also includes the algorithm that handles irregular cases to all and compares the performances of these proposed methods with original ones. This thesis thus develops a root extraction system that handles foreign Arabized words by constructing a list of about 7,000 foreign words. The outcome of the technique with best accuracy results in extracting the correct stem and root for respective words in texts, which is an enhanced rule-based method, is used in the third part of this thesis. This thesis finally proposes and implements a variant term frequency inverse document frequency weighting method, and investigates the effect of using different choices of features in document representation on single-label text classification performance (words, stems or roots as well as including to these choices their respective phrases). This thesis applies forty seven classifiers on all proposed representations and compares their performances. One challenge for researchers in Arabic text processing is that reported root extraction techniques in literature are either not accessible or require a long time to be reproduced while labeled benchmark Arabic text corpus is not fully available online. Also, by now few machine learning techniques were investigated on Arabic where usual preprocessing steps before classification were chosen. Such challenges are addressed in this thesis by developing a new labeled Arabic text corpus for extended applications of computational techniques. Results of investigated issues here show that proposing and implementing an algorithm that handles irregular words in Arabic did improve the performance of all implemented root extraction techniques. The performance of the algorithm that handles such irregular cases is evaluated in terms of accuracy improvement and execution time. Its efficiency is investigated with different document lengths and empirically is found to be linear in time for document lengths less than about 8,000. The rule-based technique is improved the highest among implemented root extraction methods when including the irregular cases handling algorithm. This thesis validates that choosing roots or stems instead of words in documents representations indeed improves single-label classification performance significantly for most used classifiers. However, the effect of extending such representations with their respective phrases on single-label text classification performance shows that it has no significant improvement. Many classifiers were not yet tested for Arabic such as the ripple-down rule classifier. The outcome of comparing the classifiers' performances concludes that the Bayesian network classifier performance is significantly the best in terms of accuracy, training time, and root mean square error values for all proposed and implemented representations.
Petra University, Amman (Jordan)

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15

Al-Nashashibi, May Yacoub Adib. "Arabic language processing for text classification : contributions to Arabic root extraction techniques, building an Arabic corpus, and to Arabic text classification techniques." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/6326.

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Анотація:

The impact and dynamics of Internet-based resources for Arabic-speaking users is increasing in significance, depth and breadth at highest pace than ever, and thus requires updated mechanisms for computational processing of Arabic texts. Arabic is a complex language and as such requires in depth investigation for analysis and improvement of available automatic processing techniques such as root extraction methods or text classification techniques, and for developing text collections that are already labeled, whether with single or multiple labels. This thesis proposes new ideas and methods to improve available automatic processing techniques for Arabic texts. Any automatic processing technique would require data in order to be used and critically reviewed and assessed, and here an attempt to develop a labeled Arabic corpus is also proposed. This thesis is composed of three parts: 1- Arabic corpus development, 2- proposing, improving and implementing root extraction techniques, and 3- proposing and investigating the effect of different pre-processing methods on single-labeled text classification methods for Arabic. This thesis first develops an Arabic corpus that is prepared to be used here for testing root extraction methods as well as single-label text classification techniques. It also enhances a rule-based root extraction method by handling irregular cases (that appear in about 34% of texts). It proposes and implements two expanded algorithms as well as an adjustment for a weight-based method. It also includes the algorithm that handles irregular cases to all and compares the performances of these proposed methods with original ones. This thesis thus develops a root extraction system that handles foreign Arabized words by constructing a list of about 7,000 foreign words. The outcome of the technique with best accuracy results in extracting the correct stem and root for respective words in texts, which is an enhanced rule-based method, is used in the third part of this thesis. This thesis finally proposes and implements a variant term frequency inverse document frequency weighting method, and investigates the effect of using different choices of features in document representation on single-label text classification performance (words, stems or roots as well as including to these choices their respective phrases). This thesis applies forty seven classifiers on all proposed representations and compares their performances. One challenge for researchers in Arabic text processing is that reported root extraction techniques in literature are either not accessible or require a long time to be reproduced while labeled benchmark Arabic text corpus is not fully available online. Also, by now few machine learning techniques were investigated on Arabic where usual preprocessing steps before classification were chosen. Such challenges are addressed in this thesis by developing a new labeled Arabic text corpus for extended applications of computational techniques. Results of investigated issues here show that proposing and implementing an algorithm that handles irregular words in Arabic did improve the performance of all implemented root extraction techniques. The performance of the algorithm that handles such irregular cases is evaluated in terms of accuracy improvement and execution time. Its efficiency is investigated with different document lengths and empirically is found to be linear in time for document lengths less than about 8,000. The rule-based technique is improved the highest among implemented root extraction methods when including the irregular cases handling algorithm. This thesis validates that choosing roots or stems instead of words in documents representations indeed improves single-label classification performance significantly for most used classifiers. However, the effect of extending such representations with their respective phrases on single-label text classification performance shows that it has no significant improvement. Many classifiers were not yet tested for Arabic such as the ripple-down rule classifier. The outcome of comparing the classifiers' performances concludes that the Bayesian network classifier performance is significantly the best in terms of accuracy, training time, and root mean square error values for all proposed and implemented representations.

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16

Lund, Max. "Duplicate Detection and Text Classification on Simplified Technical English." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158714.

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This thesis investigates the most effective way of performing classification of text labels and clustering of duplicate texts in technical documentation written in Simplified Technical English. Pre-trained language models from transformers (BERT) were tested against traditional methods such as tf-idf with cosine similarity (kNN) and SVMs on the classification task. For detecting duplicate texts, vector representations from pre-trained transformer and LSTM models were tested against tf-idf using the density-based clustering algorithms DBSCAN and HDBSCAN. The results show that traditional methods are comparable to pre-trained models for classification, and that using tf-idf vectors with a low distance threshold in DBSCAN is preferable for duplicate detection.

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17

鈴木, 祐介, Yusuke Suzuki, 茂樹松原, Shigeki Matsubara, 正俊吉川 та Masatoshi Yoshikswa. "アンカーテキストとハイパーリンクに基づくWeb 文書の階層的分類". 人工知能学会, 2005. http://hdl.handle.net/2237/97.

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18

Fearn, Wilson Murray. "Exploring the Relationship Between Vocabulary Scaling and Algorithmic Performance in Text Classification for Large Datasets." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/9053.

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Text analysis is a significant branch of natural language processing, and includes manydifferent sub-fields such as topic modeling, document classification, and sentiment analysis.Unsurprisingly, those who do text analysis are concerned with the runtime of their algorithmsSome of these algorithms have runtimes that depend jointly on the size of the corpus beinganalyzed, as well as the size of that corpus's vocabulary. Trivially, a user may reduce theamount of data they feed into their model to speed it up, but we assume that users will behesitant to do this as more data tends to lead to better model quality. On the other hand,when the runtime also depends on the vocabulary of the corpus, a user may instead modifythe vocabulary to attain a faster runtime. Because elements of the vocabulary also add tomodel quality, this puts users into the position of needing to modify the corpus vocabulary inorder to reduce the runtime of their algorithm while maintaining model quality. To this end,we look at the relationship between model quality and runtime for text analysis by looking atthe effect that current techniques in vocabulary reduction have on algorithmic runtime andcomparing that with their effect on model quality. Despite the fact that this is an importantrelationship to investigate, it appears little work has been done in this area. We find thatmost preprocessing methods do not have much of an effect on more modern algorithms, butproper rare word filtering gives the best results in the form of significant runtime reductionstogether with slight improvements in accuracy and a vocabulary size that scales efficiently aswe increase the size of the data.

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19

Calarota, Gabriele. "Domain-specific word embeddings for ICD-9-CM classification." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16714/.

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In this work we evaluate domain-speci�c embedding models induced from textual resources in the medical domain. The International Classi�cation of Diseases (ICD) is a standard, broadly used classi�cation system, that codes a large number of speci�c diseases, symptoms, injuries and medical procedures into numerical classes. Assigning a code to a clinical case means classifying that case into one or more particular discrete class, hence allowing further statistics studies and automated calculations. The possibility to have a discrete code instead of a text in natural language is intuitively a great advantage for data processing systems. The use of such classi�cation is becoming increasingly important for, but not limited to, economic and policy-making purposes. Experiments show that domain-speci�c word embeddings, instead of a general one, improves classi�ers in terms of frequency similarities between words.

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20

Bouaziz, Ameni. "Méthodes d’apprentissage interactif pour la classification des messages courts." Thesis, Université Côte d'Azur (ComUE), 2017. http://www.theses.fr/2017AZUR4039/document.

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La classification automatique des messages courts est de plus en plus employée de nos jours dans diverses applications telles que l'analyse des sentiments ou la détection des « spams ». Par rapport aux textes traditionnels, les messages courts, comme les tweets et les SMS, posent de nouveaux défis à cause de leur courte taille, leur parcimonie et leur manque de contexte, ce qui rend leur classification plus difficile. Nous présentons dans cette thèse deux nouvelles approches visant à améliorer la classification de ce type de message. Notre première approche est nommée « forêts sémantiques ». Dans le but d'améliorer la qualité des messages, cette approche les enrichit à partir d'une source externe construite au préalable. Puis, pour apprendre un modèle de classification, contrairement à ce qui est traditionnellement utilisé, nous proposons un nouvel algorithme d'apprentissage qui tient compte de la sémantique dans le processus d'induction des forêts aléatoires. Notre deuxième contribution est nommée « IGLM » (Interactive Generic Learning Method). C'est une méthode interactive qui met récursivement à jour les forêts en tenant compte des nouvelles données arrivant au cours du temps, et de l'expertise de l'utilisateur qui corrige les erreurs de classification. L'ensemble de ce mécanisme est renforcé par l'utilisation d'une méthode d'abstraction permettant d'améliorer la qualité des messages. Les différentes expérimentations menées en utilisant ces deux méthodes ont permis de montrer leur efficacité. Enfin, la dernière partie de la thèse est consacrée à une étude complète et argumentée de ces deux prenant en compte des critères variés tels que l'accuracy, la rapidité, etc
Automatic short text classification is more and more used nowadays in various applications like sentiment analysis or spam detection. Short texts like tweets or SMS are more challenging than traditional texts. Therefore, their classification is more difficult owing to their shortness, sparsity and lack of contextual information. We present two new approaches to improve short text classification. Our first approach is "Semantic Forest". The first step of this approach proposes a new enrichment method that uses an external source of enrichment built in advance. The idea is to transform a short text from few words to a larger text containing more information in order to improve its quality before building the classification model. Contrarily to the methods proposed in the literature, the second step of our approach does not use traditional learning algorithm but proposes a new one based on the semantic links among words in the Random Forest classifier. Our second contribution is "IGLM" (Interactive Generic Learning Method). It is a new interactive approach that recursively updates the classification model by considering the new data arriving over time and by leveraging the user intervention to correct misclassified data. An abstraction method is then combined with the update mechanism to improve short text quality. The experiments performed on these two methods show their efficiency and how they outperform traditional algorithms in short text classification. Finally, the last part of the thesis concerns a complete and argued comparative study of the two proposed methods taking into account various criteria such as accuracy, speed, etc

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21

Tran, Thi Quynh Nhi. "Robust and comprehensive joint image-text representations." Thesis, Paris, CNAM, 2017. http://www.theses.fr/2017CNAM1096/document.

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La présente thèse étudie la modélisation conjointe des contenus visuels et textuels extraits à partir des documents multimédias pour résoudre les problèmes intermodaux. Ces tâches exigent la capacité de ``traduire'' l'information d'une modalité vers une autre. Un espace de représentation commun, par exemple obtenu par l'Analyse Canonique des Corrélation ou son extension kernelisée est une solution généralement adoptée. Sur cet espace, images et texte peuvent être représentés par des vecteurs de même type sur lesquels la comparaison intermodale peut se faire directement.Néanmoins, un tel espace commun souffre de plusieurs déficiences qui peuvent diminuer la performance des ces tâches. Le premier défaut concerne des informations qui sont mal représentées sur cet espace pourtant très importantes dans le contexte de la recherche intermodale. Le deuxième défaut porte sur la séparation entre les modalités sur l'espace commun, ce qui conduit à une limite de qualité de traduction entre modalités. Pour faire face au premier défaut concernant les données mal représentées, nous avons proposé un modèle qui identifie tout d'abord ces informations et puis les combine avec des données relativement bien représentées sur l'espace commun. Les évaluations sur la tâche d'illustration de texte montrent que la prise en compte de ces information fortement améliore les résultats de la recherche intermodale. La contribution majeure de la thèse se concentre sur la séparation entre les modalités sur l'espace commun pour améliorer la performance des tâches intermodales. Nous proposons deux méthodes de représentation pour les documents bi-modaux ou uni-modaux qui regroupent à la fois des informations visuelles et textuelles projetées sur l'espace commun. Pour les documents uni-modaux, nous suggérons un processus de complétion basé sur un ensemble de données auxiliaires pour trouver les informations correspondantes dans la modalité absente. Ces informations complémentaires sont ensuite utilisées pour construire une représentation bi-modale finale pour un document uni-modal. Nos approches permettent d'obtenir des résultats de l'état de l'art pour la recherche intermodale ou la classification bi-modale et intermodale
This thesis investigates the joint modeling of visual and textual content of multimedia documents to address cross-modal problems. Such tasks require the ability to match information across modalities. A common representation space, obtained by eg Kernel Canonical Correlation Analysis, on which images and text can be both represented and directly compared is a generally adopted solution.Nevertheless, such a joint space still suffers from several deficiencies that may hinder the performance of cross-modal tasks. An important contribution of this thesis is therefore to identify two major limitations of such a space. The first limitation concerns information that is poorly represented on the common space yet very significant for a retrieval task. The second limitation consists in a separation between modalities on the common space, which leads to coarse cross-modal matching. To deal with the first limitation concerning poorly-represented data, we put forward a model which first identifies such information and then finds ways to combine it with data that is relatively well-represented on the joint space. Evaluations on emph{text illustration} tasks show that by appropriately identifying and taking such information into account, the results of cross-modal retrieval can be strongly improved. The major work in this thesis aims to cope with the separation between modalities on the joint space to enhance the performance of cross-modal tasks.We propose two representation methods for bi-modal or uni-modal documents that aggregate information from both the visual and textual modalities projected on the joint space. Specifically, for uni-modal documents we suggest a completion process relying on an auxiliary dataset to find the corresponding information in the absent modality and then use such information to build a final bi-modal representation for a uni-modal document. Evaluations show that our approaches achieve state-of-the-art results on several standard and challenging datasets for cross-modal retrieval or bi-modal and cross-modal classification

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22

Walker, Briana Shanise. "Rethinking Document Classification: A Pilot for the Application of Text Mining Techniques To Enhance Standardized Assessment Protocols for Critical Care Medical Team Transfer of Care." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1496760037827537.

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23

Alkhatib, Wael [Verfasser], Ralf [Akademischer Betreuer] Steinmetz, and Steffen [Akademischer Betreuer] Staab. "Semantically Enhanced and Minimally Supervised Models for Ontology Construction, Text Classification, and Document Recommendation / Wael Alkhatib ; Ralf Steinmetz, Steffen Staab." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2020. http://d-nb.info/1216997691/34.

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24

Elagouni, Khaoula. "Combining neural-based approaches and linguistic knowledge for text recognition in multimedia documents." Thesis, Rennes, INSA, 2013. http://www.theses.fr/2013ISAR0013/document.

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Les travaux de cette thèse portent sur la reconnaissance des indices textuels dans les images et les vidéos. Dans ce cadre, nous avons conçu des prototypes d'OCR (optical character recognition) capables de reconnaître tant des textes incrustés que des textes de scène acquis n'importe où au sein d'images ou de vidéos. Nous nous sommes intéressée à la définition d'approches robustes à la variabilité des textes et aux conditions d'acquisition. Plus précisément, nous avons proposé deux types de méthodes dédiées à la reconnaissance de texte : - une approche fondée sur une segmentation en caractères qui recherche des séparations non linéaires entre les caractères adaptées à la morphologie de ces derniers ; - deux approches se passant de la segmentation en intégrant un processus de scanning multi-échelles ; la première utilise un modèle de graphe pour reconnaître les textes tandis que la seconde intègre un modèle connexionniste récurrent spécifiquement développé pour gérer les contraintes spatiales entre les caractères.Outre les originalités de chacune des approches, deux contributions supplémentaires de ce travail résident dans la définition d'une reconnaissance de caractères fondée sur un modèle de classification neuronale et l'intégration de certaines connaissances linguistiques permettant de tirer profit du contexte lexical. Les différentes méthodes conçues ont été évaluées sur deux bases de documents : une base de textes incrustés dans des vidéos et une base publique de textes de scène. Les expérimentations ont permis de montrer la robustesse des approches et de comparer leurs performances à celles de l'état de l'art, mettant en évidence leurs avantages et leurs limites
This thesis focuses on the recognition of textual clues in images and videos. In this context, OCR (optical character recognition) systems, able to recognize caption texts as well as natural scene texts captured anywhere in the environment have been designed. Novel approaches, robust to text variability (differentfonts, colors, sizes, etc.) and acquisition conditions (complex background, non uniform lighting, low resolution, etc.) have been proposed. In particular, two kinds of methods dedicated to text recognition are provided:- A segmentation-based approach that computes nonlinear separations between characters well adapted to the localmorphology of images;- Two segmentation-free approaches that integrate a multi-scale scanning scheme. The first one relies on a graph model, while the second one uses a particular connectionist recurrent model able to handle spatial constraints between characters.In addition to the originalities of each approach, two extra contributions of this work lie in the design of a character recognition method based on a neural classification model and the incorporation of some linguistic knowledge that enables to take into account the lexical context.The proposed OCR systems were tested and evaluated on two datasets: a caption texts video dataset and a natural scene texts dataset (namely the public database ICDAR 2003). Experiments have demonstrated the efficiency of our approaches and have permitted to compare their performances to those of state-of-the-art methods, highlighting their advantages and limits

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25

Risch, Jean-Charles. "Enrichissement des Modèles de Classification de Textes Représentés par des Concepts." Thesis, Reims, 2017. http://www.theses.fr/2017REIMS012/document.

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Анотація:

La majorité des méthodes de classification de textes utilisent le paradigme du sac de mots pour représenter les textes. Pourtant cette technique pose différents problèmes sémantiques : certains mots sont polysémiques, d'autres peuvent être des synonymes et être malgré tout différenciés, d'autres encore sont liés sémantiquement sans que cela soit pris en compte et enfin, certains mots perdent leur sens s'ils sont extraits de leur groupe nominal. Pour pallier ces problèmes, certaines méthodes ne représentent plus les textes par des mots mais par des concepts extraits d'une ontologie de domaine, intégrant ainsi la notion de sens au modèle. Les modèles intégrant la représentation des textes par des concepts restent peu utilisés à cause des résultats peu satisfaisants. Afin d'améliorer les performances de ces modèles, plusieurs méthodes ont été proposées pour enrichir les caractéristiques des textes à l'aide de nouveaux concepts extraits de bases de connaissances. Mes travaux donnent suite à ces approches en proposant une étape d'enrichissement des modèles à l'aide d'une ontologie de domaine associée. J'ai proposé deux mesures permettant d'estimer l'appartenance aux catégories de ces nouveaux concepts. A l'aide de l'algorithme du classifieur naïf Bayésien, j'ai testé et comparé mes contributions sur le corpus de textes labéllisés Ohsumed et l'ontologie de domaine Disease Ontology. Les résultats satisfaisants m'ont amené à analyser plus précisément le rôle des relations sémantiques dans l'enrichissement des modèles. Ces nouveaux travaux ont été le sujet d'une seconde expérience où il est question d'évaluer les apports des relations hiérarchiques d'hyperonymie et d'hyponymie
Most of text-classification methods use the ``bag of words” paradigm to represent texts. However Bloahdom and Hortho have identified four limits to this representation: (1) some words are polysemics, (2) others can be synonyms and yet differentiated in the analysis, (3) some words are strongly semantically linked without being taken into account in the representation as such and (4) certain words lose their meaning if they are extracted from their nominal group. To overcome these problems, some methods no longer represent texts with words but with concepts extracted from a domain ontology (Bag of Concept), integrating the notion of meaning into the model. Models integrating the bag of concepts remain less used because of the unsatisfactory results, thus several methods have been proposed to enrich text features using new concepts extracted from knowledge bases. My work follows these approaches by proposing a model-enrichment step using a domain ontology, I proposed two measures to estimate to belong to the categories of these new concepts. Using the naive Bayes classifier algorithm, I tested and compared my contributions on the Ohsumed corpus using the domain ontology ``Disease Ontology”. The satisfactory results led me to analyse more precisely the role of semantic relations in the enrichment step. These new works have been the subject of a second experiment in which we evaluate the contributions of the hierarchical relations of hypernymy and hyponymy

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26

Synek, Radovan. "Klasifikace textu pomocí metody SVM." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237229.

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This thesis deals with text mining. It focuses on problems of document classification and related techniques, mainly data preprocessing. Project also introduces the SVM method, which has been chosen for classification, design and testing of implemented application.

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Sullivan, Daniel Edward. "Evaluation of Word and Paragraph Embeddings and Analogical Reasoning as an Alternative to Term Frequency-Inverse Document Frequency-based Classification in Support of Biocuration." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/80572.

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This research addresses the problem, can unsupervised learning generate a representation that improves on the commonly used term frequency-inverse document frequency (TF-IDF ) representation by capturing semantic relations? The analysis measures the quality of sentence classification using term TF-IDF representations, and finds a practical upper limit to precision and recall in a biomedical text classification task (F1-score of 0.85). Arguably, one could use ontologies to supplement TF-IDF, but ontologies are sparse in coverage and costly to create. This prompts a correlated question: can unsupervised learning capture semantic relations at least as well as existing ontologies, and thus supplement existing sparse ontologies? A shallow neural network implementing the Skip-Gram algorithm is used to generate semantic vectors using a corpus of approximately 2.4 billion words. The ability to capture meaning is assessed by comparing semantic vectors generated with MESH. Results indicate that semantic vectors trained by unsupervised methods capture comparable levels of semantic features in some cases, such as amino acid (92% of similarity represented in MESH), but perform substantially poorer in more expansive topics, such as pathogenic bacteria (37.8% similarity represented in MESH). Possible explanations for this difference in performance are proposed along with a method to combine manually curated ontologies with semantic vector spaces to produce a more comprehensive representation than either alone. Semantic vectors are also used as representations for paragraphs, which, when used for classification, achieve an F1-score of 0.92. The results of classification and analogical reasoning tasks are promising but a formal model of semantic vectors, subject to the constraints of known linguistic phenomenon, is needed. This research includes initial steps for developing a formal model of semantic vectors based on a combination of linear algebra and fuzzy set theory subject to the semantic molecularism linguistic model. This research is novel in its analysis of semantic vectors applied to the biomedical domain, analysis of different performance characteristics in biomedical analogical reasoning tasks, comparison semantic relations captured by between vectors and MESH, and the initial development of a formal model of semantic vectors.
Ph. D.

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Ghanmi, Nabil. "Segmentation d'images de documents manuscrits composites : application aux documents de chimie." Thesis, Université de Lorraine, 2016. http://www.theses.fr/2016LORR0109/document.

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Cette thèse traite de la segmentation structurelle de documents issus de cahiers de chimie. Ce travail est utile pour les chimistes en vue de prendre connaissance des conditions des expériences réalisées. Les documents traités sont manuscrits, hétérogènes et multi-scripteurs. Bien que leur structure physique soit relativement simple, une succession de trois régions représentant : la formule chimique de l’expérience, le tableau des produits utilisés et un ou plusieurs paragraphes textuels décrivant le déroulement de l’expérience, les lignes limitrophes des régions portent souvent à confusion, ajouté à cela des irrégularités dans la disposition des cellules du tableau, rendant le travail de séparation un vrai défi. La méthodologie proposée tient compte de ces difficultés en opérant une segmentation à plusieurs niveaux de granularité, et en traitant la segmentation comme un problème de classification. D’abord, l’image du document est segmentée en structures linéaires à l’aide d’un lissage horizontal approprié. Le seuil horizontal combiné avec une tolérance verticale avantage le regroupement des éléments fragmentés de la formule sans trop fusionner le texte. Ces structures linéaires sont classées en Texte ou Graphique en s’appuyant sur des descripteurs structurels spécifiques, caractéristiques des deux classes. Ensuite, la segmentation est poursuivie sur les lignes textuelles pour séparer les lignes du tableau de celles de la description. Nous avons proposé pour cette classification un modèle CAC qui permet de déterminer la séquence optimale d’étiquettes associées à la séquence des lignes d’un document. Le choix de ce type de modèle a été motivé par sa capacité à absorber la variabilité des lignes et à exploiter les informations contextuelles. Enfin, pour le problème de la segmentation de tableaux en cellules, nous avons proposé une méthode hybride qui fait coopérer deux niveaux d’analyse : structurel et syntaxique. Le premier s’appuie sur la présence des lignes graphiques et de l’alignement de texte et d’espaces ; et le deuxième tend à exploiter la cohérence de la syntaxe très réglementée du contenu des cellules. Nous avons proposé, dans ce cadre, une approche contextuelle pour localiser les champs numériques dans le tableau, avec reconnaissance des chiffres isolés et connectés. La thèse étant effectuée dans le cadre d’une convention CIFRE, en collaboration avec la société eNovalys, nous avons implémenté et testé les différentes étapes du système sur une base conséquente de documents de chimie
This thesis deals with chemistry document segmentation and structure analysis. This work aims to help chemists by providing the information on the experiments which have already been carried out. The documents are handwritten, heterogeneous and multi-writers. Although their physical structure is relatively simple, since it consists of a succession of three regions representing: the chemical formula of the experiment, a table of the used products and one or more text blocks describing the experimental procedure, several difficulties are encountered. In fact, the lines located at the region boundaries and the imperfections of the table layout make the separation task a real challenge. The proposed methodology takes into account these difficulties by performing segmentation at several levels and treating the region separation as a classification problem. First, the document image is segmented into linear structures using an appropriate horizontal smoothing. The horizontal threshold combined with a vertical overlapping tolerance favor the consolidation of fragmented elements of the formula without too merge the text. These linear structures are classified in text or graphic based on discriminant structural features. Then, the segmentation is continued on text lines to separate the rows of the table from the lines of the raw text locks. We proposed for this classification, a CRF model for determining the optimal labelling of the line sequence. The choice of this kind of model has been motivated by its ability to absorb the variability of lines and to exploit contextual information. For the segmentation of table into cells, we proposed a hybrid method that includes two levels of analysis: structural and syntactic. The first relies on the presence of graphic lines and the alignment of both text and spaces. The second tends to exploit the coherence of the cell content syntax. We proposed, in this context, a Recognition-based approach using contextual knowledge to detect the numeric fields present in the table. The thesis was carried out in the framework of CIFRE, in collaboration with the eNovalys campany.We have implemented and tested all the steps of the proposed system on a consequent dataset of chemistry documents

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Albitar, Shereen. "De l'usage de la sémantique dans la classification supervisée de textes : application au domaine médical." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4343/document.

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Анотація:

Cette thèse porte sur l’impact de l’usage de la sémantique dans le processus de la classification supervisée de textes. Cet impact est évalué au travers d’une étude expérimentale sur des documents issus du domaine médical et en utilisant UMLS (Unified Medical Language System) en tant que ressource sémantique. Cette évaluation est faite selon quatre scénarii expérimentaux d’ajout de sémantique à plusieurs niveaux du processus de classification. Le premier scénario correspond à la conceptualisation où le texte est enrichi avant indexation par des concepts correspondant dans UMLS ; le deuxième et le troisième scénario concernent l’enrichissement des vecteurs représentant les textes après indexation dans un sac de concepts (BOC – bag of concepts) par des concepts similaires. Enfin le dernier scénario utilise la sémantique au niveau de la prédiction des classes, où les concepts ainsi que les relations entre eux, sont impliqués dans la prise de décision. Le premier scénario est testé en utilisant trois des méthodes de classification: Rocchio, NB et SVM. Les trois autres scénarii sont uniquement testés en utilisant Rocchio qui est le mieux à même d’accueillir les modifications nécessaires. Au travers de ces différentes expérimentations nous avons tout d’abord montré que des améliorations significatives pouvaient être obtenues avec la conceptualisation du texte avant l’indexation. Ensuite, à partir de représentations vectorielles conceptualisées, nous avons constaté des améliorations plus modérées avec d’une part l’enrichissement sémantique de cette représentation vectorielle après indexation, et d’autre part l’usage de mesures de similarité sémantique en prédiction
The main interest of this research is the effect of using semantics in the process of supervised text classification. This effect is evaluated through an experimental study on documents related to the medical domain using the UMLS (Unified Medical Language System) as a semantic resource. This evaluation follows four scenarios involving semantics at different steps of the classification process: the first scenario incorporates the conceptualization step where text is enriched with corresponding concepts from UMLS; both the second and the third scenarios concern enriching vectors that represent text as Bag of Concepts (BOC) with similar concepts; the last scenario considers using semantics during class prediction, where concepts as well as the relations between them are involved in decision making. We test the first scenario using three popular classification techniques: Rocchio, NB and SVM. We choose Rocchio for the other scenarios for its extendibility with semantics. According to experiment, results demonstrated significant improvement in classification performance using conceptualization before indexing. Moderate improvements are reported using conceptualized text representation with semantic enrichment after indexing or with semantic text-to-text semantic similarity measures for prediction

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Salah, Aghiles. "Von Mises-Fisher based (co-)clustering for high-dimensional sparse data : application to text and collaborative filtering data." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB093/document.

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Анотація:

La classification automatique, qui consiste à regrouper des objets similaires au sein de groupes, également appelés classes ou clusters, est sans aucun doute l’une des méthodes d’apprentissage non-supervisé les plus utiles dans le contexte du Big Data. En effet, avec l’expansion des volumes de données disponibles, notamment sur le web, la classification ne cesse de gagner en importance dans le domaine de la science des données pour la réalisation de différentes tâches, telles que le résumé automatique, la réduction de dimension, la visualisation, la détection d’anomalies, l’accélération des moteurs de recherche, l’organisation d’énormes ensembles de données, etc. De nombreuses méthodes de classification ont été développées à ce jour, ces dernières sont cependant fortement mises en difficulté par les caractéristiques complexes des ensembles de données que l’on rencontre dans certains domaines d’actualité tel que le Filtrage Collaboratif (FC) et de la fouille de textes. Ces données, souvent représentées sous forme de matrices, sont de très grande dimension (des milliers de variables) et extrêmement creuses (ou sparses, avec plus de 95% de zéros). En plus d’être de grande dimension et sparse, les données rencontrées dans les domaines mentionnés ci-dessus sont également de nature directionnelles. En effet, plusieurs études antérieures ont démontré empiriquement que les mesures directionnelles, telle que la similarité cosinus, sont supérieurs à d’autres mesures, telle que la distance Euclidiennes, pour la classification des documents textuels ou pour mesurer les similitudes entre les utilisateurs/items dans le FC. Cela suggère que, dans un tel contexte, c’est la direction d’un vecteur de données (e.g., représentant un document texte) qui est pertinente, et non pas sa longueur. Il est intéressant de noter que la similarité cosinus est exactement le produit scalaire entre des vecteurs unitaires (de norme 1). Ainsi, d’un point de vue probabiliste l’utilisation de la similarité cosinus revient à supposer que les données sont directionnelles et réparties sur la surface d’une hypersphère unité. En dépit des nombreuses preuves empiriques suggérant que certains ensembles de données sparses et de grande dimension sont mieux modélisés sur une hypersphère unité, la plupart des modèles existants dans le contexte de la fouille de textes et du FC s’appuient sur des hypothèses populaires : distributions Gaussiennes ou Multinomiales, qui sont malheureusement inadéquates pour des données directionnelles. Dans cette thèse, nous nous focalisons sur deux challenges d’actualité, à savoir la classification des documents textuels et la recommandation d’items, qui ne cesse d’attirer l’attention dans les domaines de la fouille de textes et celui du filtrage collaborative, respectivement. Afin de répondre aux limitations ci-dessus, nous proposons une série de nouveaux modèles et algorithmes qui s’appuient sur la distribution de von Mises-Fisher (vMF) qui est plus appropriée aux données directionnelles distribuées sur une hypersphère unité
Cluster analysis or clustering, which aims to group together similar objects, is undoubtedly a very powerful unsupervised learning technique. With the growing amount of available data, clustering is increasingly gaining in importance in various areas of data science for several reasons such as automatic summarization, dimensionality reduction, visualization, outlier detection, speed up research engines, organization of huge data sets, etc. Existing clustering approaches are, however, severely challenged by the high dimensionality and extreme sparsity of the data sets arising in some current areas of interest, such as Collaborative Filtering (CF) and text mining. Such data often consists of thousands of features and more than 95% of zero entries. In addition to being high dimensional and sparse, the data sets encountered in the aforementioned domains are also directional in nature. In fact, several previous studies have empirically demonstrated that directional measures—that measure the distance between objects relative to the angle between them—, such as the cosine similarity, are substantially superior to other measures such as Euclidean distortions, for clustering text documents or assessing the similarities between users/items in CF. This suggests that in such context only the direction of a data vector (e.g., text document) is relevant, not its magnitude. It is worth noting that the cosine similarity is exactly the scalar product between unit length data vectors, i.e., L 2 normalized vectors. Thus, from a probabilistic perspective using the cosine similarity is equivalent to assuming that the data are directional data distributed on the surface of a unit-hypersphere. Despite the substantial empirical evidence that certain high dimensional sparse data sets, such as those encountered in the above domains, are better modeled as directional data, most existing models in text mining and CF are based on popular assumptions such as Gaussian, Multinomial or Bernoulli which are inadequate for L 2 normalized data. In this thesis, we focus on the two challenging tasks of text document clustering and item recommendation, which are still attracting a lot of attention in the domains of text mining and CF, respectively. In order to address the above limitations, we propose a suite of new models and algorithms which rely on the von Mises-Fisher (vMF) assumption that arises naturally for directional data lying on a unit-hypersphere

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Poulain, d'Andecy Vincent. "Système à connaissance incrémentale pour la compréhension de document et la détection de fraude." Thesis, La Rochelle, 2021. http://www.theses.fr/2021LAROS025.

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Анотація:

Le Document Understanding est la discipline de l’Intelligence Artificielle qui dote les machines du pouvoir de Lecture. Cette capacité sous-entend de comprendre dans une vision globale l’objet du document, sa classe, et dans une vision locale, des informations précises, des entités. Un double défi est de réussir ces opérations dans plus de 90% des cas tout en éduquant la machine avec le moins d’effort humain possible. Cette thèse défend la possibilité de relever ces défis avec des méthodes à apprentissage incrémental. Nos propositions permettent d’éduquer efficacement et itérativement une machine avec quelques exemples de document. Pour la classification, nous démontrons (1) la possibilité de découvrir itérativement des descripteurs textuels, (2) l’intérêt de tenir compte de l’ordre du discours et (3) l’intérêt d’intégrer dans le modèle de donnée incrémental une mémoire épisodique de quelques Souvenirs d’échantillon. Pour l’extraction d’entité, nous démontrons un modèle structurel itératif à partir d’un graphe en étoile dont la robustesse est améliorée avec quelques connaissances a priori d’ordre général. Conscient de l’importance économique et sociétale de la fraude dans les flux documentaires, cette thèse fait également le point sur cette problématique. Notre contribution est modeste en étudiant les catégories de fraude pour ouvrir des perspectives de recherche. Cette thèse a été conduite dans un cadre atypique en conjonction avec une activité industrielle à Yooz et des projets collaboratifs, en particulier, les projets FEDER SECURDOC soutenu par la région Nouvelle Aquitaine et Labcom IDEAS soutenu par l’ANR
The Document Understanding is the Artificial Intelligence ability for machines to Read documents. In a global vision, it aims the understanding of the document function, the document class, and in a more local vision, it aims the understanding of some specific details like entities. The scientific challenge is to recognize more than 90% of the data. While the industrial challenge requires this performance with the least human effort to train the machine. This thesis defends that Incremental Learning methods can cope with both challenges. The proposals enable an efficient iterative training with very few document samples. For the classification task, we demonstrate (1) the continue learning of textual descriptors, (2) the benefit of the discourse sequence, (3) the benefit of integrating a Souvenir of few samples in the knowledge model. For the data extraction task, we demonstrate an iterative structural model, based on a star-graph representation, which is enhanced by the embedding of few a priori knowledges. Aware about economic and societal impacts because the document fraud, this thesis deals with this issue too. Our modest contribution is only to study the different fraud categories to open further research. This research work has been done in a non-classic framework, in conjunction of industrial activities for Yooz and collaborative research projects like the FEDER Securdoc project supported by la région Nouvelle Aquitaine, and the Labcom IDEAS supported by the ANR

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Tiepmar, Jochen. "Release of the MySQL based implementation of the CTS protocol." Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-201773.

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In a project called "A Library of a Billion Words" we needed an implementation of the CTS protocol that is capable of handling a text collection containing at least 1 billion words. Because the existing solutions did not work for this scale or were still in development I started an implementation of the CTS protocol using methods that MySQL provides. Last year we published a paper that introduced a prototype with the core functionalities without being compliant with the specifications of CTS (Tiepmar et al., 2013). The purpose of this paper is to describe and evaluate the MySQL based implementa-tion now that it is fulfilling the specifications version 5.0 rc.1 and mark it as finished and ready to use. Fur-ther information, online instances of CTS for all de-scribed datasets and binaries can be accessed via the projects website1. Reference Tiepmar J, Teichmann C, Heyer G, Berti M and Crane G. 2013. A new Implementation for Canonical Text Services. in Proceedings of the 8th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH).

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Григораш, Вадим Святославович, та Vadym Hryhorash. "Комп’ютеризована система тематичної рубрикації документів". Bachelor's thesis, Тернопільський національний технічний університет імені Івана Пулюя, 2021. http://elartu.tntu.edu.ua/handle/lib/35428.

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Анотація:

У кваліфікаційній роботі спроектовано та реалізовано програмний прототип комп’ютеризованої системи тематичної рубрикації текстових документів. До складу системи входять сховище документів та компонент, що відповідає з рубрикацію документів на основі аналізу їх вмісту. Компонент рубрикації документів складається з наступних модулів: модуль попереднього опрацювання тексту; модуль виявлення ознак тексту; модуль класифікації документів. В якості методів для виявлення ознак тексту у документі запропоновано використати різновиди статистичних ознак алгоритму TF-IDF, а також семантична векторизації.
In the qualification work the program prototype of the computerized system of thematic rubrication of text documents is designed and realized. The system includes a document repository and a component that corresponds to the rubrication of documents based on the analysis of their content. The component of rubrication of documents consists of the following modules: the module of preliminary processing of the text; text feature detection module; document classification module. As methods for detecting the features of the text in the document, it is proposed to use a variety of statistical features of the TF-IDF algorithm, as well as semantic vectorization.
Вступ. 1. Аналіз технічного завдання і сфери застосування комп’ютеризованої системи тема- тичної рубрикації документів 2. Проектування структури і компонентів комп’ютеризованої системи тематичної рубрикації документів. 3. Програмна реалізація комп’ютеризованої системи тематичної рубрикації документів. 4. Безпека життєдіяльності, основи охорони праці. Висновки

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Liaghat, Zeinab. "Quality-efficiency trade-offs in machine learning applied to text processing." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/402575.

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Анотація:

Nowadays, the amount of available digital documents is rapidly growing, expanding at a considerable rate and coming from a variety of sources. Sources of unstructured and semi-structured information include the World Wide Web, news articles, biological databases, electronic mail, digital libraries, governmental digital repositories, chat rooms, online forums, blogs, and social media such as Facebook, Instagram, LinkedIn, Pinterest, Twitter, YouTube, Instagram, Pinterest, plus many others. Extracting information from these resources and finding useful information from such collections has become a challenge, which makes organizing massive amounts of data a necessity. Data mining, machine learning, and natural language processing are powerful techniques that can be used together to deal with this big challenge. Depending on the task or problem at hand, there are many different approaches that can be used. The methods that are being implemented are continuously being optimized, but not all these methods have been tested and compared for quality after training on large size corpora for supervised machine learning algorithms. The question is what happens to the quality of methods if we increase the data size from, say, 100 MB to over 1 GB? Moreover, are quality gains worth it when the rate of data processing diminishes? Can we trade quality for time efficiency and recover the quality loss by just being able to process more data? This thesis is first attempt to answer these questions in a general way for text processing tasks, as not enough research has been done to compare those methods considering the trade-offs of data size, quality, and processing time. Hence, we propose a trade-off analysis framework and apply it to three important text processing problems: Named Entity Recognition, Sentiment Analysis, and Document Classification. These problems were also chosen because they have different levels of object granularity: words, passages, and documents. For each problem, we select several machine learning algorithms and we evaluate the trade-offs of these different methods on large publicly available datasets (news, reviews, patents). We use different data subsets of increasing size ranging from 50 MB to a few GB, to explore these trade-offs. We conclude, as hypothesized, that just because the method has good performance in small data, it does not necessarily have the same performance for big data. For the two last problems, we consider similar algorithms and also consider two different data sets and two different evaluation techniques, to study the impact of the data and the evaluation technique on the resulting trade-offs. We find that the results do not change significantly.
Avui en dia, la quantitat de documents digitals disponibles està creixent ràpidament, expandint- se a un ritme considerable i procedint de diverses fonts. Les fonts d’informació no estructurada i semiestructurada inclouen la World Wide Web, articles de notícies, bases de dades biològiques, correus electrònics, biblioteques digitals, repositoris electrònics governamentals, , sales de xat, forums en línia, blogs i mitjans socials com Facebook, Instagram, LinkedIn, Pinterest, Twitter, YouTube i molts d’altres. Extreure’n informació d’aquests recursos i trobar informació útil d’aquestes col.leccions s’ha convertit en un desafiament que fa que l’organització d’aquesta enorme quantitat de dades esdevingui una necessitat. La mineria de dades, l’aprenentatge automàtic i el processament del llenguatge natural són tècniques poderoses que poden utilitzar-se conjuntament per fer front a aquest gran desafiament. Segons la tasca o el problema en qüestió existeixen molts emfo- caments diferents que es poden utilitzar. Els mètodes que s’estan implementant s’optimitzen continuament, però aquests mètodes d’aprenentatge automàtic supervisats han estat provats i comparats amb grans dades d’entrenament. La pregunta és : Què passa amb la qualitat dels mètodes si incrementem les dades de 100 MB a 1 GB? Més encara: Les millores en la qualitat valen la pena quan la taxa de processament de les dades minva? Podem canviar qualitat per eficiència, tot recuperant la perdua de qualitat quan processem més dades? Aquesta tesi és una primera aproximació per resoldre aquestes preguntes de forma gene- ral per a tasques de processament de text, ja que no hi ha hagut suficient investigació per a comparar aquests mètodes considerant el balanç entre el tamany de les dades, la qualitat dels resultats i el temps de processament. Per tant, proposem un marc per analitzar aquest balanç i l’apliquem a tres problemes importants de processament de text: Reconeixement d’Entitats Anomenades, Anàlisi de Sentiments i Classificació de Documents. Aquests problemes tam- bé han estat seleccionats perquè tenen nivells diferents de granularitat: paraules, opinions i documents complerts. Per a cada problema seleccionem diferents algoritmes d’aprenentatge automàtic i avaluem el balanç entre aquestes variables per als diferents algoritmes en grans conjunts de dades públiques ( notícies, opinions, patents). Utilitzem subconjunts de diferents tamanys entre 50 MB i alguns GB per a explorar aquests balanç. Per acabar, com havíem suposat, no perquè un algoritme és eficient en poques dades serà eficient en grans quantitats de dades. Per als dos últims problemes considerem algoritmes similars i també dos conjunts diferents de dades i tècniques d’avaluació per a estudiar l’impacte d’aquests dos paràmetres en els resultats. Mostrem que els resultats no canvien significativament amb aquests canvis.
Hoy en día, la cantidad de documentos digitales disponibles está creciendo rápidamente, ex- pandiéndose a un ritmo considerable y procediendo de una variedad de fuentes. Estas fuentes de información no estructurada y semi estructurada incluyen la World Wide Web, artículos de noticias, bases de datos biológicos, correos electrónicos, bibliotecas digitales, repositorios electrónicos gubernamentales, salas de chat, foros en línea, blogs y medios sociales como Fa- cebook, Instagram, LinkedIn, Pinterest, Twitter, YouTube, además de muchos otros. Extraer información de estos recursos y encontrar información útil de tales colecciones se ha convertido en un desafío que hace que la organización de esa enorme cantidad de datos sea una necesidad. La minería de datos, el aprendizaje automático y el procesamiento del lenguaje natural son técnicas poderosas que pueden utilizarse conjuntamente para hacer frente a este gran desafío. Dependiendo de la tarea o el problema en cuestión, hay muchos enfoques dife- rentes que se pueden utilizar. Los métodos que se están implementando se están optimizando continuamente, pero estos métodos de aprendizaje automático supervisados han sido probados y comparados con datos de entrenamiento grandes. La pregunta es ¿Qué pasa con la calidad de los métodos si incrementamos los datos de 100 MB a 1GB? Más aún, ¿las mejoras en la cali- dad valen la pena cuando la tasa de procesamiento de los datos disminuye? ¿Podemos cambiar calidad por eficiencia, recuperando la perdida de calidad cuando procesamos más datos? Esta tesis es una primera aproximación para resolver estas preguntas de forma general para tareas de procesamiento de texto, ya que no ha habido investigación suficiente para comparar estos métodos considerando el balance entre el tamaño de los datos, la calidad de los resultados y el tiempo de procesamiento. Por lo tanto, proponemos un marco para analizar este balance y lo aplicamos a tres importantes problemas de procesamiento de texto: Reconocimiento de En- tidades Nombradas, Análisis de Sentimientos y Clasificación de Documentos. Estos problemas fueron seleccionados también porque tienen distintos niveles de granularidad: palabras, opinio- nes y documentos completos. Para cada problema seleccionamos distintos algoritmos de apren- dizaje automático y evaluamos el balance entre estas variables para los distintos algoritmos en grandes conjuntos de datos públicos (noticias, opiniones, patentes). Usamos subconjuntos de distinto tamaño entre 50 MB y varios GB para explorar este balance. Para concluir, como ha- bíamos supuesto, no porque un algoritmo es eficiente en pocos datos será eficiente en grandes cantidades de datos. Para los dos últimos problemas consideramos algoritmos similares y tam- bién dos conjuntos distintos de datos y técnicas de evaluación, para estudiar el impacto de estos dos parámetros en los resultados. Mostramos que los resultados no cambian significativamente con estos cambios.

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Ailem, Melissa. "Sparsity-sensitive diagonal co-clustering algorithms for the effective handling of text data." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB087.

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Dans le contexte actuel, il y a un besoin évident de techniques de fouille de textes pour analyser l'énorme quantité de documents textuelles non structurées disponibles sur Internet. Ces données textuelles sont souvent représentées par des matrices creuses (sparses) de grande dimension où les lignes et les colonnes représentent respectivement des documents et des termes. Ainsi, il serait intéressant de regrouper de façon simultanée ces termes et documents en classes homogènes, rendant ainsi cette quantité importante de données plus faciles à manipuler et à interpréter. Les techniques de classification croisée servent justement cet objectif. Bien que plusieurs techniques existantes de co-clustering ont révélé avec succès des blocs homogènes dans plusieurs domaines, ces techniques sont toujours contraintes par la grande dimensionalité et la sparsité caractérisant les matrices documents-termes. En raison de cette sparsité, plusieurs co-clusters sont principalement composés de zéros. Bien que ces derniers soient homogènes, ils ne sont pas pertinents et doivent donc être filtrés en aval pour ne garder que les plus importants. L'objectif de cette thèse est de proposer de nouveaux algorithmes de co-clustering conçus pour tenir compte des problèmes liés à la sparsité mentionnés ci-dessus. Ces algorithmes cherchent une structure diagonale par blocs et permettent directement d'identifier les co-clusters les plus pertinents, ce qui les rend particulièrement efficaces pour le co-clustering de données textuelles. Dans ce contexte, nos contributions peuvent être résumées comme suit: Tout d'abord, nous introduisons et démontrons l'efficacité d'un nouvel algorithme de co-clustering basé sur la maximisation directe de la modularité de graphes. Alors que les algorithmes de co-clustering existants qui se basent sur des critères de graphes utilisent des approximations spectrales, l'algorithme proposé utilise une procédure d'optimisation itérative pour révéler les co-clusters les plus pertinents dans une matrice documents-termes. Par ailleurs, l'optimisation proposée présente l'avantage d'éviter le calcul de vecteurs propres, qui est une tâche rédhibitoire lorsque l'on considère des données de grande dimension. Ceci est une amélioration par rapport aux approches spectrales, où le calcul des vecteurs propres est nécessaire pour effectuer le co-clustering. Dans un second temps, nous utilisons une approche probabiliste pour découvrir des structures en blocs homogènes diagonaux dans des matrices documents-termes. Nous nous appuyons sur des approches de type modèles de mélanges, qui offrent de solides bases théoriques et une grande flexibilité qui permet de découvrir diverses structures de co-clusters. Plus précisément, nous proposons un modèle de blocs latents parcimonieux avec des distributions de Poisson sous contraintes. De façon intéressante, ce modèle comprend la sparsité dans sa formulation, ce qui le rend particulièrement adapté aux données textuelles. En plaçant l'estimation des paramètres de ce modèle dans le cadre du maximum de vraisemblance et du maximum de vraisemblance classifiante, quatre algorithmes de co-clustering ont été proposées, incluant une variante dure, floue, stochastique et une quatrième variante qui tire profit des avantages des variantes floue et stochastique simultanément. Pour finir, nous proposons un nouveau cadre de fouille de textes biomédicaux qui comprend certains algorithmes de co-clustering mentionnés ci-dessus. Ce travail montre la contribution du co-clustering dans une problématique réelle de fouille de textes biomédicaux. Le cadre proposé permet de générer de nouveaux indices sur les résultats retournés par les études d'association pan-génomique (GWAS) en exploitant les abstracts de la base de données PUBMED. (...)
In the current context, there is a clear need for Text Mining techniques to analyse the huge quantity of unstructured text documents available on the Internet. These textual data are often represented by sparse high dimensional matrices where rows and columns represent documents and terms respectively. Thus, it would be worthwhile to simultaneously group these terms and documents into meaningful clusters, making this substantial amount of data easier to handle and interpret. Co-clustering techniques just serve this purpose. Although many existing co-clustering approaches have been successful in revealing homogeneous blocks in several domains, these techniques are still challenged by the high dimensionality and sparsity characteristics exhibited by document-term matrices. Due to this sparsity, several co-clusters are primarily composed of zeros. While homogeneous, these co-clusters are irrelevant and must be filtered out in a post-processing step to keep only the most significant ones. The objective of this thesis is to propose new co-clustering algorithms tailored to take into account these sparsity-related issues. The proposed algorithms seek a block diagonal structure and allow to straightaway identify the most useful co-clusters, which makes them specially effective for the text co-clustering task. Our contributions can be summarized as follows: First, we introduce and demonstrate the effectiveness of a novel co-clustering algorithm based on a direct maximization of graph modularity. While existing graph-based co-clustering algorithms rely on spectral relaxation, the proposed algorithm uses an iterative alternating optimization procedure to reveal the most meaningful co-clusters in a document-term matrix. Moreover, the proposed optimization has the advantage of avoiding the computation of eigenvectors, a task which is prohibitive when considering high dimensional data. This is an improvement over spectral approaches, where the eigenvectors computation is necessary to perform the co-clustering. Second, we use an even more powerful approach to discover block diagonal structures in document-term matrices. We rely on mixture models, which offer strong theoretical foundations and considerable flexibility that makes it possible to uncover various specific cluster structure. More precisely, we propose a rigorous probabilistic model based on the Poisson distribution and the well known Latent Block Model. Interestingly, this model includes the sparsity in its formulation, which makes it particularly effective for text data. Setting the estimate of this model’s parameters under the Maximum Likelihood (ML) and the Classification Maximum Likelihood (CML) approaches, four co-clustering algorithms have been proposed, including a hard, a soft, a stochastic and a fourth algorithm which leverages the benefits of both the soft and stochastic variants, simultaneously. As a last contribution of this thesis, we propose a new biomedical text mining framework that includes some of the above mentioned co-clustering algorithms. This work shows the contribution of co-clustering in a real biomedical text mining problematic. The proposed framework is able to propose new clues about the results of genome wide association studies (GWAS) by mining PUBMED abstracts. This framework has been tested on asthma disease and allowed to assess the strength of associations between asthma genes reported in previous GWAS as well as discover new candidate genes likely associated to asthma. In a nutshell, while several text co-clustering algorithms already exist, their performance can be substantially increased if more appropriate models and algorithms are available. According to the extensive experiments done on several challenging real-world text data sets, we believe that this thesis has served well this objective

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Franco, Salvador Marc. "A Cross-domain and Cross-language Knowledge-based Representation of Text and its Meaning." Doctoral thesis, Universitat Politècnica de València, 2017. http://hdl.handle.net/10251/84285.

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Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human languages. One of its most challenging aspects involves enabling computers to derive meaning from human natural language. To do so, several meaning or context representations have been proposed with competitive performance. However, these representations still have room for improvement when working in a cross-domain or cross-language scenario. In this thesis we study the use of knowledge graphs as a cross-domain and cross-language representation of text and its meaning. A knowledge graph is a graph that expands and relates the original concepts belonging to a set of words. We obtain its characteristics using a wide-coverage multilingual semantic network as knowledge base. This allows to have a language coverage of hundreds of languages and millions human-general and -specific concepts. As starting point of our research we employ knowledge graph-based features - along with other traditional ones and meta-learning - for the NLP task of single- and cross-domain polarity classification. The analysis and conclusions of that work provide evidence that knowledge graphs capture meaning in a domain-independent way. The next part of our research takes advantage of the multilingual semantic network and focuses on cross-language Information Retrieval (IR) tasks. First, we propose a fully knowledge graph-based model of similarity analysis for cross-language plagiarism detection. Next, we improve that model to cover out-of-vocabulary words and verbal tenses and apply it to cross-language document retrieval, categorisation, and plagiarism detection. Finally, we study the use of knowledge graphs for the NLP tasks of community questions answering, native language identification, and language variety identification. The contributions of this thesis manifest the potential of knowledge graphs as a cross-domain and cross-language representation of text and its meaning for NLP and IR tasks. These contributions have been published in several international conferences and journals.
El Procesamiento del Lenguaje Natural (PLN) es un campo de la informática, la inteligencia artificial y la lingüística computacional centrado en las interacciones entre las máquinas y el lenguaje de los humanos. Uno de sus mayores desafíos implica capacitar a las máquinas para inferir el significado del lenguaje natural humano. Con este propósito, diversas representaciones del significado y el contexto han sido propuestas obteniendo un rendimiento competitivo. Sin embargo, estas representaciones todavía tienen un margen de mejora en escenarios transdominios y translingües. En esta tesis estudiamos el uso de grafos de conocimiento como una representación transdominio y translingüe del texto y su significado. Un grafo de conocimiento es un grafo que expande y relaciona los conceptos originales pertenecientes a un conjunto de palabras. Sus propiedades se consiguen gracias al uso como base de conocimiento de una red semántica multilingüe de amplia cobertura. Esto permite tener una cobertura de cientos de lenguajes y millones de conceptos generales y específicos del ser humano. Como punto de partida de nuestra investigación empleamos características basadas en grafos de conocimiento - junto con otras tradicionales y meta-aprendizaje - para la tarea de PLN de clasificación de la polaridad mono- y transdominio. El análisis y conclusiones de ese trabajo muestra evidencias de que los grafos de conocimiento capturan el significado de una forma independiente del dominio. La siguiente parte de nuestra investigación aprovecha la capacidad de la red semántica multilingüe y se centra en tareas de Recuperación de Información (RI). Primero proponemos un modelo de análisis de similitud completamente basado en grafos de conocimiento para detección de plagio translingüe. A continuación, mejoramos ese modelo para cubrir palabras fuera de vocabulario y tiempos verbales, y lo aplicamos a las tareas translingües de recuperación de documentos, clasificación, y detección de plagio. Por último, estudiamos el uso de grafos de conocimiento para las tareas de PLN de respuesta de preguntas en comunidades, identificación del lenguaje nativo, y identificación de la variedad del lenguaje. Las contribuciones de esta tesis ponen de manifiesto el potencial de los grafos de conocimiento como representación transdominio y translingüe del texto y su significado en tareas de PLN y RI. Estas contribuciones han sido publicadas en diversas revistas y conferencias internacionales.
El Processament del Llenguatge Natural (PLN) és un camp de la informàtica, la intel·ligència artificial i la lingüística computacional centrat en les interaccions entre les màquines i el llenguatge dels humans. Un dels seus majors reptes implica capacitar les màquines per inferir el significat del llenguatge natural humà. Amb aquest propòsit, diverses representacions del significat i el context han estat proposades obtenint un rendiment competitiu. No obstant això, aquestes representacions encara tenen un marge de millora en escenaris trans-dominis i trans-llenguatges. En aquesta tesi estudiem l'ús de grafs de coneixement com una representació trans-domini i trans-llenguatge del text i el seu significat. Un graf de coneixement és un graf que expandeix i relaciona els conceptes originals pertanyents a un conjunt de paraules. Les seves propietats s'aconsegueixen gràcies a l'ús com a base de coneixement d'una xarxa semàntica multilingüe d'àmplia cobertura. Això permet tenir una cobertura de centenars de llenguatges i milions de conceptes generals i específics de l'ésser humà. Com a punt de partida de la nostra investigació emprem característiques basades en grafs de coneixement - juntament amb altres tradicionals i meta-aprenentatge - per a la tasca de PLN de classificació de la polaritat mono- i trans-domini. L'anàlisi i conclusions d'aquest treball mostra evidències que els grafs de coneixement capturen el significat d'una forma independent del domini. La següent part de la nostra investigació aprofita la capacitat\hyphenation{ca-pa-ci-tat} de la xarxa semàntica multilingüe i se centra en tasques de recuperació d'informació (RI). Primer proposem un model d'anàlisi de similitud completament basat en grafs de coneixement per a detecció de plagi trans-llenguatge. A continuació, vam millorar aquest model per cobrir paraules fora de vocabulari i temps verbals, i ho apliquem a les tasques trans-llenguatges de recuperació de documents, classificació, i detecció de plagi. Finalment, estudiem l'ús de grafs de coneixement per a les tasques de PLN de resposta de preguntes en comunitats, identificació del llenguatge natiu, i identificació de la varietat del llenguatge. Les contribucions d'aquesta tesi posen de manifest el potencial dels grafs de coneixement com a representació trans-domini i trans-llenguatge del text i el seu significat en tasques de PLN i RI. Aquestes contribucions han estat publicades en diverses revistes i conferències internacionals.
Franco Salvador, M. (2017). A Cross-domain and Cross-language Knowledge-based Representation of Text and its Meaning [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/84285
TESIS

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Balikas, Georgios. "Explorer et apprendre à partir de collections de textes multilingues à l'aide des modèles probabilistes latents et des réseaux profonds." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM054/document.

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Le texte est l'une des sources d'informations les plus répandues et les plus persistantes. L'analyse de contenu du texte se réfère à des méthodes d'étude et de récupération d'informations à partir de documents. Aujourd'hui, avec une quantité de texte disponible en ligne toujours croissante l'analyse de contenu du texte revêt une grande importance parce qu' elle permet une variété d'applications. À cette fin, les méthodes d'apprentissage de la représentation sans supervision telles que les modèles thématiques et les word embeddings constituent des outils importants.L'objectif de cette dissertation est d'étudier et de relever des défis dans ce domaine.Dans la première partie de la thèse, nous nous concentrons sur les modèles thématiques et plus précisément sur la manière d'incorporer des informations antérieures sur la structure du texte à ces modèles.Les modèles de sujets sont basés sur le principe du sac-de-mots et, par conséquent, les mots sont échangeables. Bien que cette hypothèse profite les calculs des probabilités conditionnelles, cela entraîne une perte d'information.Pour éviter cette limitation, nous proposons deux mécanismes qui étendent les modèles de sujets en intégrant leur connaissance de la structure du texte. Nous supposons que les documents sont répartis dans des segments de texte cohérents. Le premier mécanisme attribue le même sujet aux mots d'un segment. La seconde, capitalise sur les propriétés de copulas, un outil principalement utilisé dans les domaines de l'économie et de la gestion des risques, qui sert à modéliser les distributions communes de densité de probabilité des variables aléatoires tout en n'accédant qu'à leurs marginaux.La deuxième partie de la thèse explore les modèles de sujets bilingues pour les collections comparables avec des alignements de documents explicites. En règle générale, une collection de documents pour ces modèles se présente sous la forme de paires de documents comparables. Les documents d'une paire sont écrits dans différentes langues et sont thématiquement similaires. À moins de traductions, les documents d'une paire sont semblables dans une certaine mesure seulement. Pendant ce temps, les modèles de sujets représentatifs supposent que les documents ont des distributions thématiques identiques, ce qui constitue une hypothèse forte et limitante. Pour le surmonter, nous proposons de nouveaux modèles thématiques bilingues qui intègrent la notion de similitude interlingue des documents qui constituent les paires dans leurs processus générateurs et d'inférence.La dernière partie de la thèse porte sur l'utilisation d'embeddings de mots et de réseaux de neurones pour trois applications d'exploration de texte. Tout d'abord, nous abordons la classification du document polylinguistique où nous soutenons que les traductions d'un document peuvent être utilisées pour enrichir sa représentation. À l'aide d'un codeur automatique pour obtenir ces représentations de documents robustes, nous démontrons des améliorations dans la tâche de classification de documents multi-classes. Deuxièmement, nous explorons la classification des tweets à plusieurs tâches en soutenant que, en formant conjointement des systèmes de classification utilisant des tâches corrélées, on peut améliorer la performance obtenue. À cette fin, nous montrons comment réaliser des performances de pointe sur une tâche de classification du sentiment en utilisant des réseaux neuronaux récurrents. La troisième application que nous explorons est la récupération d'informations entre langues. Compte tenu d'un document écrit dans une langue, la tâche consiste à récupérer les documents les plus similaires à partir d'un ensemble de documents écrits dans une autre langue. Dans cette ligne de recherche, nous montrons qu'en adaptant le problème du transport pour la tâche d'estimation des distances documentaires, on peut obtenir des améliorations importantes
Text is one of the most pervasive and persistent sources of information. Content analysis of text in its broad sense refers to methods for studying and retrieving information from documents. Nowadays, with the ever increasing amounts of text becoming available online is several languages and different styles, content analysis of text is of tremendous importance as it enables a variety of applications. To this end, unsupervised representation learning methods such as topic models and word embeddings constitute prominent tools.The goal of this dissertation is to study and address challengingproblems in this area, focusing on both the design of novel text miningalgorithms and tools, as well as on studying how these tools can be applied to text collections written in a single or several languages.In the first part of the thesis we focus on topic models and more precisely on how to incorporate prior information of text structure to such models.Topic models are built on the premise of bag-of-words, and therefore words are exchangeable. While this assumption benefits the calculations of the conditional probabilities it results in loss of information.To overcome this limitation we propose two mechanisms that extend topic models by integrating knowledge of text structure to them. We assume that the documents are partitioned in thematically coherent text segments. The first mechanism assigns the same topic to the words of a segment. The second, capitalizes on the properties of copulas, a tool mainly used in the fields of economics and risk management that is used to model the joint probability density distributions of random variables while having access only to their marginals.The second part of the thesis explores bilingual topic models for comparable corpora with explicit document alignments. Typically, a document collection for such models is in the form of comparable document pairs. The documents of a pair are written in different languages and are thematically similar. Unless translations, the documents of a pair are similar to some extent only. Meanwhile, representative topic models assume that the documents have identical topic distributions, which is a strong and limiting assumption. To overcome it we propose novel bilingual topic models that incorporate the notion of cross-lingual similarity of the documents that constitute the pairs in their generative and inference processes. Calculating this cross-lingual document similarity is a task on itself, which we propose to address using cross-lingual word embeddings.The last part of the thesis concerns the use of word embeddings and neural networks for three text mining applications. First, we discuss polylingual document classification where we argue that translations of a document can be used to enrich its representation. Using an auto-encoder to obtain these robust document representations we demonstrate improvements in the task of multi-class document classification. Second, we explore multi-task sentiment classification of tweets arguing that by jointly training classification systems using correlated tasks can improve the obtained performance. To this end we show how can achieve state-of-the-art performance on a sentiment classification task using recurrent neural networks. The third application we explore is cross-lingual information retrieval. Given a document written in one language, the task consists in retrieving the most similar documents from a pool of documents written in another language. In this line of research, we show that by adapting the transportation problem for the task of estimating document distances one can achieve important improvements

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Sychra, Martin. "Analýza sentimentu s využitím dolování dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255424.

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The theme of the work is sentiment analysis, especially in terms of informatics (marginally from a linguistic point of view). The linguistic part discusses the term sentiment and language methods for its analysis, e.g. lemmatization, POS tagging, using the list of stopwords etc. More attention is paid to the structure of the sentiment analyzer which is based on some of the machine learning methods (support vector machines, Naive Bayes and maximum entropy classification). On the basis of the theoretical background, a functional analyzer is projected and implemented. The experiments are focused mainly on comparing the classification methods and on the benefits of using the individual preprocessing methods. The success rate of the constructed classifier reaches up to 84 % in the cross-validation.

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Pagliarani, Andrea. "New markov chain based methods for single and cross-domain sentiment classification." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8445/.

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Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

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Průša, Petr. "Multi-label klasifikace textových dokumentů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-412872.

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The master's thesis deals with automatic classifi cation of text document. It explains basic terms and problems of text mining. The thesis explains term clustering and shows some basic clustering algoritms. The thesis also shows some methods of classi fication and deals with matrix regression closely. Application using matrix regression for classifi cation was designed and developed. Experiments were focused on normalization and thresholding.

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Phaweni, Thembani. "Classification and visualisation of text documents using networks." Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29534.

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In both the areas of text classification and text visualisation graph/network theoretic methods can be applied effectively. For text classification we assessed the effectiveness of graph/network summary statistics to develop weighting schemes and features to improve test accuracy. For text visualisation we developed a framework using established visual cues from the graph visualisation literature to communicate information intuitively. The final output of the visualisation component of the dissertation was a tool that would allow members of the public to produce a visualisation from a text document. We represented a text document as a graph/network. The words were nodes and the edges were created when a pair of words appeared within a pre-specified distance (window) of words from each other. The text document model is a matrix representation of a document collection such that it can be integrated into a machine or statistical learning algorithm. The entries of this matrix can be weighting according to various schemes. We used the graph/network representation of a text document to create features and weighting schemes that could be applied to the text document model. This approach was not well developed for text classification therefore we applied different edge weighting methods, window sizes, weighting schemes and features. We also applied three machine learning algorithms, naïve Bayes, neural networks and support vector machines. We compared our various graph/network approaches to the traditional document model with term frequency inverse-document-frequency. We were interested in establishing whether or not the use of graph weighting schemes and graph features could increase test accuracy for text classification tasks. As far as we can tell from the literature, this is the first attempt to use graph features to weight bag-of-words features for text classification. These methods had been applied to information retrieval (Blanco & Lioma, 2012). It seemed they could also be applied to text classification. The text visualisation field seemed divorced from the text summarisation and information retrieval fields, in that text co-occurrence relationships were not treated with equal importance. Developments in the graph/network visualisation literature could be taken advantage of for the purposes of text visualisation. We created a framework for text visualisation using the graph/network representation of a text document. We used force directed algorithms to visualise the document. We used established visual cues like, colour, size and proximity in space to convey information through the visualisation. We also applied clustering and part-of-speech tagging to allow for filtering and isolating of specific information within the visualised document. We demonstrated this framework with four example texts. We found that total degree, a graph weighting scheme, outperformed term frequency on average. The effect of graph features depended heavily on the machine learning method used: for the problems we considered graph features increased accuracy for SVM classifiers, had little effect for neural networks and decreased accuracy for naïve Bayes classifiers Therefore the impact on test accuracy of adding graph features to the document model is dependent on the machine learning algorithm used. The visualisation of text graphs is able to convey meaningful information regarding the text at a glance through established visual cues. Related words are close together in visual space and often connected by thick edges. Large nodes often represent important words. Modularity clustering is able to extract thematically consistent clusters from text graphs. This allows for the clusters to be isolated and investigated individually to understand specific themes within a document. The use of part-of-speech tagging is effective in both reducing the number of words being displayed but also increasing the relevance of words being displayed. This was made clear through the use of part-of-speech tags applied to the Internal Resistance of Apartheid Wikipedia webpage. The webpage was reduced to its proper nouns which contained much of the important information in the text. Training accuracy is important in text classification which is a task that can often be performed on vast amounts of documents. Much of the research in text classification is aimed at increasing classification accuracy either through feature engineering, or optimising machine learning methods. The finding that total degree outperformed term frequency on average provides an alternative avenue for achieving higher test accuracy. The finding that the addition of graph features can increase test accuracy when matched with the right machine learning algorithm suggests some new research should be conducted regarding the role that graph features can have in text classification. Text visualisation is used as an exploratory tool and as a means of quickly and easily conveying text information. The framework we developed is able to create automated text visualisations that intuitively convey information for short and long text documents. This can greatly reduce the amount of time it takes to assess the content of a document which can increase general access to information.

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Abdaoui, Amine. "Fouille des médias sociaux français : expertise et sentiment." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT249/document.

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Les médias sociaux ont changé notre manière de communiquer entre individus, au sein des organisations et des communautés. La disponibilité de ces données sociales ouvre de nouvelles opportunités pour comprendre et influencer le comportement des utilisateurs. De ce fait, la fouille des médias sociaux connait un intérêt croissant dans divers milieux scientifiques et économiques. Dans cette thèse, nous nous intéressons spécifiquement aux utilisateurs de ces réseaux et cherchons à les caractériser selon deux axes : (i) leur expertise et leur réputation et (ii) les sentiments qu’ils expriment.De manière classique, les données sociales sont souvent fouillées selon leur structure en réseau. Cependant, le contenu textuel des messages échangés peut faire émerger des connaissances complémentaires qui ne peuvent être connues via la seule analyse de la structure. Jusqu’à récemment, la majorité des travaux concernant l’analyse du contenu textuel était proposée pour l’Anglais. L’originalité de cette thèse est de développer des méthodes et des ressources basées sur le contenu pour la fouille des réseaux sociaux pour la langue Française.Dans le premier axe, nous proposons d'abord d’identifier l'expertise des utilisateurs. Pour cela, nous avons utilisé des forums qui recrutent des experts en santé pour apprendre des modèles de classification qui servent à identifier les messages postés par les experts dans n’importe quel autre forum. Nous démontrons que les modèles appris sur des forums appropriés peuvent être utilisés efficacement sur d’autres forums. Puis, dans un second temps, nous nous intéressons à la réputation des utilisateurs dans ces forums. L’idée est de rechercher les expressions de confiance et de méfiance exprimées dans les messages, de rechercher les destinataires de ces messages et d’utiliser ces informations pour en déduire la réputation des utilisateurs. Nous proposons une nouvelle mesure de réputation qui permet de pondérer le score de chaque réponse selon la réputation de son auteur. Des évaluations automatiques et manuelles ont démontré l’efficacité de l’approche.Dans le deuxième axe, nous nous sommes focalisés sur l’extraction de sentiments (polarité et émotion). Pour cela, dans un premier temps, nous avons commencé par construire un lexique de sentiments et d’émotions pour le Français que nous appelons FEEL (French Expanded Emotion Lexicon). Ce lexique est construit de manière semi-automatique en traduisant et en étendant son homologue Anglais NRC EmoLex. Nous avons ensuite comparé FEEL avec les lexiques Français de la littérature sur des benchmarks de référence. Les résultats ont montré que FEEL permet d’améliorer la classification des textes Français selon leurs polarités et émotions. Dans un deuxième temps, nous avons proposé d’évaluer de manière assez exhaustive différentes méthodes et ressources pour la classification de sentiments en Français. Les expérimentations menées ont permis de déterminer les caractéristiques utiles dans la classification de sentiments pour différents types de textes. Les systèmes appris se sont montrés particulièrement efficaces sur des benchmarks de référence. De manière générale, ces travaux ont ouvert des perspectives prometteuses sur diverses tâches d’analyse des réseaux sociaux pour la langue française incluant: (i) combiner plusieurs sources pour transférer la connaissance sur les utilisateurs des réseaux sociaux; (ii) la fouille des réseaux sociaux en utilisant les images, les vidéos, les géolocalisations, etc. et (iii) l'analyse multilingues de sentiment
Social Media has changed the way we communicate between individuals, within organizations and communities. The availability of these social data opens new opportunities to understand and influence the user behavior. Therefore, Social Media Mining is experiencing a growing interest in various scientific and economic circles. In this thesis, we are specifically interested in the users of these networks whom we try to characterize in two ways: (i) their expertise and their reputations and (ii) the sentiments they express.Conventionally, social data is often mined according to its network structure. However, the textual content of the exchanged messages may reveal additional knowledge that can not be known through the analysis of the structure. Until recently, the majority of work done for the analysis of the textual content was proposed for English. The originality of this thesis is to develop methods and resources based on the textual content of the messages for French Social Media Mining.In the first axis, we initially suggest to predict the user expertise. For this, we used forums that recruit health experts to learn classification models that serve to identify messages posted by experts in any other health forum. We demonstrate that models learned on appropriate forums can be used effectively on other forums. Then, in a second step, we focus on the user reputation in these forums. The idea is to seek expressions of trust and distrust expressed in the textual content of the exchanged messages, to search the recipients of these messages and use this information to deduce users' reputation. We propose a new reputation measure that weighs the score of each response by the reputation of its author. Automatic and manual evaluations have demonstrated the effectiveness of the proposed approach.In the second axis, we focus on the extraction of sentiments (emotions and polarity). For this, we started by building a French lexicon of sentiments and emotions that we call FEEL (French Expanded Emotions Lexicon). This lexicon is built semi-automatically by translating and expanding its English counterpart NRC EmoLex. We then compare FEEL with existing French lexicons from literature on reference benchmarks. The results show that FEEL improves the classification of French texts according to their polarities and emotions. Finally, we propose to evaluate different features, methods and resources for the classification of sentiments in French. The conducted experiments have identified useful features and methods in the classification of sentiments for different types of texts. The learned systems have been particularly efficient on reference benchmarks.Generally, this work opens promising perspectives on various analytical tasks of Social Media Mining including: (i) combining multiple sources in mining Social Media users; (ii) multi-modal Social Media Mining using not just text but also image, videos, location, etc. and (iii) multilingual sentiment analysis

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Pitou, Cynthia. "Extraction d'informations textuelles au sein de documents numérisés : cas des factures." Thesis, La Réunion, 2017. http://www.theses.fr/2017LARE0015.

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Le traitement automatique de documents consiste en la transformation dans un format compréhensible par un système informatique de données présentes au sein de documents et compréhensibles par l'Homme. L'analyse de document et la compréhension de documents sont les deux phases du processus de traitement automatique de documents. Étant donnée une image de document constituée de mots, de lignes et d'objets graphiques tels que des logos, l'analyse de documents consiste à extraire et isoler les mots, les lignes et les objets, puis à les regrouper au sein de blocs. Les différents blocs ainsi formés constituent la structure géométrique du document. La compréhension de documents fait correspondre à cette structure géométrique une structure logique en considérant des liaisons logiques (à gauche, à droite, au-dessus, en-dessous) entre les objets du document. Un système de traitement de documents doit être capable de : (i) localiser une information textuelle, (ii) identifier si cette information est pertinente par rapport aux autres informations contenues dans le document, (iii) extraire cette information dans un format compréhensible par un programme informatique. Pour la réalisation d'un tel système, les difficultés à surmonter sont liées à la variabilité des caractéristiques de documents, telles que le type (facture, formulaire, devis, rapport, etc.), la mise en page (police, style, agencement), la langue, la typographie et la qualité de numérisation du document. Dans ce mémoire, nous considérons en particulier des documents numérisés, également connus sous le nom d'images de documents. Plus précisément, nous nous intéressons à la localisation d'informations textuelles au sein d'images de factures, afin de les extraire à l'aide d'un moteur de reconnaissance de caractères. Les factures sont des documents très utilisés mais non standards. En effet, elles contiennent des informations obligatoires (le numéro de facture, le numéro siret de l'émetteur, les montants, etc.) qui, selon l'émetteur, peuvent être localisées à des endroits différents. Les contributions présentées dans ce mémoire s'inscrivent dans le cadre de la localisation et de l'extraction d'informations textuelles fondées sur des régions identifiées au sein d'une image de document.Tout d'abord, nous présentons une approche de décomposition d'une image de documents en sous-régions fondée sur la décomposition quadtree. Le principe de cette approche est de décomposer une image de documents en quatre sous-régions, de manière récursive, jusqu'à ce qu'une information textuelle d'intérêt soit extraite à l'aide d'un moteur de reconnaissance de caractères. La méthode fondée sur cette approche, que nous proposons, permet de déterminer efficacement les régions contenant une information d'intérêt à extraire.Dans une autre approche, incrémentale et plus flexible, nous proposons un système d'extraction d'informations textuelles qui consiste en un ensemble de régions prototypes et de chemins pour parcourir ces régions prototypes. Le cycle de vie de ce système comprend cinq étapes:- Construction d'un jeu de données synthétiques à partir d'images de factures réelles contenant les informations d'intérêts.- Partitionnement des données produites.- Détermination des régions prototypes à partir de la partition obtenue.- Détermination des chemins pour parcourir les régions prototypes, à partir du treillis de concepts d'un contexte formel convenablement construit.- Mise à jour du système de manière incrémentale suite à l'insertion de nouvelles données
Document processing is the transformation of a human understandable data in a computer system understandable format. Document analysis and understanding are the two phases of document processing. Considering a document containing lines, words and graphical objects such as logos, the analysis of such a document consists in extracting and isolating the words, lines and objects and then grouping them into blocks. The subsystem of document understanding builds relationships (to the right, left, above, below) between the blocks. A document processing system must be able to: locate textual information, identify if that information is relevant comparatively to other information contained in the document, extract that information in a computer system understandable format. For the realization of such a system, major difficulties arise from the variability of the documents characteristics, such as: the type (invoice, form, quotation, report, etc.), the layout (font, style, disposition), the language, the typography and the quality of scanning.This work is concerned with scanned documents, also known as document images. We are particularly interested in locating textual information in invoice images. Invoices are largely used and well regulated documents, but not unified. They contain mandatory information (invoice number, unique identifier of the issuing company, VAT amount, net amount, etc.) which, depending on the issuer, can take various locations in the document. The present work is in the framework of region-based textual information localization and extraction.First, we present a region-based method guided by quadtree decomposition. The principle of the method is to decompose the images of documents in four equals regions and each regions in four new regions and so on. Then, with a free optical character recognition (OCR) engine, we try to extract precise textual information in each region. A region containing a number of expected textual information is not decomposed further. Our method allows to determine accurately in document images, the regions containing text information that one wants to locate and retrieve quickly and efficiently.In another approach, we propose a textual information extraction model consisting in a set of prototype regions along with pathways for browsing through these prototype regions. The life cycle of the model comprises five steps:- Produce synthetic invoice data from real-world invoice images containing the textual information of interest, along with their spatial positions.- Partition the produced data.- Derive the prototype regions from the obtained partition clusters.- Derive pathways for browsing through the prototype regions, from the concept lattice of a suitably defined formal context.- Update incrementally the set of protype regions and the set of pathways, when one has to add additional data

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Tagny, Ngompe Gildas. "Méthodes D'Analyse Sémantique De Corpus De Décisions Jurisprudentielles." Thesis, IMT Mines Alès, 2020. http://www.theses.fr/2020EMAL0002.

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Une jurisprudence est un corpus de décisions judiciaires représentant la manière dont sont interprétées les lois pour résoudre un contentieux. Elle est indispensable pour les juristes qui l'analysent pour comprendre et anticiper la prise de décision des juges. Son analyse exhaustive est difficile manuellement du fait de son immense volume et de la nature non-structurée des documents. L'estimation du risque judiciaire par des particuliers est ainsi impossible car ils sont en outre confrontés à la complexité du système et du langage judiciaire. L'automatisation de l'analyse des décisions permet de retrouver exhaustivement des connaissances pertinentes pour structurer la jurisprudence à des fins d'analyses descriptives et prédictives. Afin de rendre la compréhension d'une jurisprudence exhaustive et plus accessible, cette thèse aborde l'automatisation de tâches importantes pour l'analyse métier des décisions judiciaires. En premier, est étudiée l'application de modèles probabilistes d'étiquetage de séquences pour la détection des sections qui structurent les décisions de justice, d'entités juridiques, et de citations de lois. Ensuite, l'identification des demandes des parties est étudiée. L'approche proposée pour la reconnaissance des quanta demandés et accordés exploite la proximité entre les sommes d'argent et des termes-clés appris automatiquement. Nous montrons par ailleurs que le sens du résultat des juges est identifiable soit à partir de termes-clés prédéfinis soit par une classification des décisions. Enfin, pour une catégorie donnée de demandes, les situations ou circonstances factuelles où sont formulées ces demandes sont découvertes par regroupement non supervisé des décisions. A cet effet, une méthode d'apprentissage d'une distance de similarité est proposée et comparée à des distances établies. Cette thèse discute des résultats expérimentaux obtenus sur des données réelles annotées manuellement. Le mémoire propose pour finir une démonstration d'applications à l'analyse descriptive d'un grand corpus de décisions judiciaires françaises
A case law is a corpus of judicial decisions representing the way in which laws are interpreted to resolve a dispute. It is essential for lawyers who analyze it to understand and anticipate the decision-making of judges. Its exhaustive analysis is difficult manually because of its immense volume and the unstructured nature of the documents. The estimation of the judicial risk by individuals is thus impossible because they are also confronted with the complexity of the judicial system and language. The automation of decision analysis enable an exhaustive extraction of relevant knowledge for structuring case law for descriptive and predictive analyses. In order to make the comprehension of a case law exhaustive and more accessible, this thesis deals with the automation of some important tasks for the expert analysis of court decisions. First, we study the application of probabilistic sequence labeling models for the detection of the sections that structure court decisions, legal entities, and legal rules citations. Then, the identification of the demands of the parties is studied. The proposed approach for the recognition of the requested and granted quanta exploits the proximity between sums of money and automatically learned key-phrases. We also show that the meaning of the judges' result is identifiable either from predefined keywords or by a classification of decisions. Finally, for a given category of demands, the situations or factual circumstances in which those demands are made, are discovered by clustering the decisions. For this purpose, a method of learning a similarity distance is proposed and compared with established distances. This thesis discusses the experimental results obtained on manually annotated real data. Finally, the thesis proposes a demonstration of applications to the descriptive analysis of a large corpus of French court decisions

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Alvarenga, Leonel Diógenes Carvalhaes. "Uso de Seleção de Características da Wikipedia na Classificação Automática de Textos." Universidade Federal de Goiás, 2012. http://repositorio.bc.ufg.br/tede/handle/tde/2870.

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The traditional methods of text classification typically represent documents only as a set of words, also known as "Bag of Words"(BOW). Several studies have shown good results on making use of thesauri and encyclopedias as external information sources, aiming to expand the BOW representation by the identification of synonymy and hyponymy relationships between present terms in a document collection. However, the expansion process may introduce terms that lead to an erroneous classification. In this paper, we propose the use of feature selection measures in order to select features extracted from Wikipedia in order to improve the efectiveness of the expansion process. The study also proposes a feature selection measure called Tendency Factor to One Category (TF1C), so that the experiments showed that this measure proves to be competitive with the other measures Information Gain, Gain Ratio and Chisquared, in the process, delivering the best gains in microF1 and macroF1, in most experiments. The full use of features selected in this process showed to be more stable in assisting the classification, while it showed lower performance on restricting its insertion only to documents of the classes in which these features are well punctuated by the selection measures. When applied in the Reuters-21578, Ohsumed first - 20000 and 20Newsgroups collections, our approach to feature selection allowed the reduction of noise insertion inherent in the expansion process, and improved the results of use hyponyms, and demonstrated that the synonym relationship from Wikipedia can also be used in the document expansion, increasing the efectiveness of the automatic text classification.
Os métodos tradicionais de classificação de textos normalmente representam documentos apenas como um conjunto de palavras, também conhecido como BOW (do inglês, Bag of Words). Vários estudos têm mostrado bons resultados ao utilizar-se de tesauros e enciclopédias como fontes externas de informações, objetivando expandir a representação BOW a partir da identificação de relacionamentos de sinonômia e hiponômia entre os termos presentes em uma coleção de documentos. Todavia, o processo de expansão pode introduzir termos que conduzam a uma classificação errônea do documento. No presente trabalho, propõe-se a aplicação de medidas de avaliação de termos para a seleção de características extraídas da Wikipédia, com o objetivo de melhorar a eficácia de sua utilização durante o processo de expansão de documentos. O estudo também propõe uma medida de seleção de características denominada Fator de Tendência a uma Categoria (FT1C), de modo que os experimentos realizados demonstraram que esta medida apresenta desempenho competitivo com as medidas Information Gain, Gain Ratio e Chi-squared, neste processo, apresentando os melhores ganhos de microF1 e macroF1, na maioria dos experimentos realizados. O uso integral das características selecionadas neste processo, demonstrou auxiliar a classificação de forma mais estável, ao passo que apresentou menor desempenho ao se restringir sua inserção somente aos documentos das classes em que estas características são bem pontuadas pelas medidas de seleção. Ao ser aplicada nas coleções Reuters-21578, Ohsumed rst-20000 e 20Newsgroups, a abordagem com seleção de características permitiu a redução da inserção de ruídos inerentes do processo de expansão e potencializou o uso de hipônimos, assim como demonstrou que as relações de sinonômia da Wikipédia também podem ser utilizadas na expansão de documentos, elevando a eficácia da classificação automática de textos.

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Saad, Mohammed Fathi Hassan. "A cluster classification method for the extraction of knowledge from text documents." Thesis, University of East Anglia, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.446513.

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Ke, Guiyao. "Mesures de comparabilité pour la construction assistée de corpus comparables bilingues thématiques." Phd thesis, Université de Bretagne Sud, 2014. http://tel.archives-ouvertes.fr/tel-00997837.

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Les corpus comparables thématiques regroupent des textes issus d¡¯un même thème et rédigés dans plusieurs langues, fortement similaires mais ne comprenant pas de traductions mutuelles. Par rapport aux corpus parallèles qui regroupent des paires de traductions, les corpus comparables présentent trois avantages: premièrement, ce sont des ressources riches et larges : en volume et en période couverte; deuxièmement, les corpus comparables fournissent des ressources linguistiques originales et thématiques. Enfin, ils sont moins coûteux à développer que les corpus parallèles. Avec le développement considérable du WEB, une matière première très abondante est exploitable pour la construction de corpus comparables. En contre-partie, la qualité des corpus comparables est essentielle pour leur utilisation dans différents domaines tels que la traduction automatique ou assistée, l¡¯extraction de terminologies bilingues, la recherche d¡¯information multilingue, etc. L¡¯objectif de ce travail de thèse est de développer une approche méthodologique et un outillage informatique pour fournir une assistance à la construction des corpus comparables bilingues et thématiques de ? bonne qualité ?, à partir du WEB et à la demande. Nous présentons tout d¡¯abord la notion de mesure de comparabilité qui associe deux espaces linguistiques et, à partir d¡¯une mesure quantitative de comparabilité de référence, nous proposons deux variantes, qualifiées de comparabilité thématique, que nous évaluons suivant un protocole basé sur la dégradation progressive d¡¯un corpus parallèle. Nous proposons ensuite une nouvelle méthode pour améliorer le co-clustering et la co-classification de documents bilingues, ainsi que l¡¯alignement des clusters comparables. Celle-ci fusionne des similarités natives définies dans chacun des espaces linguistiques avec des similarités induites par la mesure de comparabilité utilisée. Enfin, nous proposons une démarche intégrée basée sur les contributions précédemment évoquées afin d¡¯assister la construction, à partir du WEB, de corpus comparables bilingues thématiques de qualité. Cette démarche comprend une étape de validation manuelle pour garantir la qualité de l¡¯alignement des clusters comparables. En jouant sur le seuil de comparabilité d¡¯alignement, différents corpus comparables associés à des niveaux de comparabilité variables peuvent être fournis en fonction des besoins spécifiés. Les expérimentations que nous avons menées sur des Flux RSS issus de grands quotidiens internationaux apparaissent pertinentes et prometteuses.

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Tsatsaronis, George. "An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition." BioMed Central, 2015. https://tud.qucosa.de/id/qucosa%3A29496.

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Анотація:

This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies.

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49

Teboul, Bruno. "Le développement du neuromarketing aux Etats-Unis et en France. Acteurs-réseaux, traces et controverses." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLED036/document.

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Notre travail de recherche explore de manière comparée le développement du neuromarketing aux Etats-Unis et en France. Nous commençons par analyser la littérature sur le neuromarketing. Nous utilisons comme cadre théorique et méthodologique l’Actor Network Theory (ANT) ou Théorie de l’Acteur-Réseau (dans le sillage des travaux de Bruno Latour et Michel Callon). Nous montrons ainsi comment des actants « humains et non-humains »: acteurs-réseaux, traces (publications) et controverses forment les piliers d’une nouvelle discipline telle que le neuromarketing. Notre approche hybride « qualitative-quantitative », nous permet de construire une méthodologie appliquée de l’ANT: analyse bibliométrique (Publish Or Perish), text mining, clustering et analyse sémantique de la littérature scientifique et web du neuromarketing. A partir de ces résultats, nous construisons des cartographies, sous forme de graphes en réseau (Gephi) qui révèlent les interrelations et les associations entre acteurs, traces et controverses autour du neuromarketing
Our research explores the comparative development of neuromarketing between the United States and France. We start by analyzing the literature on neuromarketing. We use as theoretical and methodological framework the Actor Network Theory (ANT) (in the wake of the work of Bruno Latour and Michel Callon). We show how “human and non-human” entities (“actants”): actor-network, traces (publications) and controversies form the pillars of a new discipline such as the neuromarketing. Our hybrid approach “qualitative-quantitative” allows us to build an applied methodology of the ANT: bibliometric analysis (Publish Or Perish), text mining, clustering and semantic analysis of the scientific literature and web of the neuromarketing. From these results, we build data visualizations, mapping of network graphs (Gephi) that reveal the interrelations and associations between actors, traces and controversies about neuromarketing

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50

Tsatsaronis, George. "An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-202687.

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Анотація:

This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies.

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