Relevant bibliographies by topics / Text modeling

Academic literature on the topic 'Text modeling'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Text modeling"

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Bell, Timothy, Ian H. Witten, and John G. Cleary. "Modeling for text compression." ACM Computing Surveys 21, no. 4 (December 1989): 557–91. http://dx.doi.org/10.1145/76894.76896.

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Leboeuf, J. ‐N, D. K. Lee, B. A. Carreras, N. Dominguez, J. H. Harris, C. L. Hedrick, C. Hidalgo, et al. "TEXT tokamak edge turbulence modeling." Physics of Fluids B: Plasma Physics 3, no. 8 (August 1991): 2291–99. http://dx.doi.org/10.1063/1.859596.

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CHEN, YE-SHO. "ZIPF'S LAWS IN TEXT MODELING." International Journal of General Systems 15, no. 3 (August 1989): 233–52. http://dx.doi.org/10.1080/03081078908935048.

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Kartika, Rica, and Yulianti Rasyid. "PENGARUH TEKNIK PEMODELAN TERHADAP KETERAMPILAN MENULIS TEKS PROSEDUR SISWA KELAS VII SMP NEGERI 14 PADANG." Pendidikan Bahasa Indonesia 8, no. 2 (June 10, 2019): 81. http://dx.doi.org/10.24036/104518-019883.

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ABSTRACT The purpose of this research is three. First, describing the procedure of writing the text of a class VII junior high School 14 Padang before using modelling techniques. Second, describing the procedure of writing the text of Class VII in SMP Negeri 14 Padang after using modeling technique. Third, describing the influence of use of modeling techniques to the skill level of writing the text procedures class VII SMP Negeri 14 Padang. This type of research is quantitative research with experimental methods. This research draft is one group Pretests and posttest design. The population in this study is a grade VII student at SMP Negeri 14 Padang enrolled in Lesson 2018/2019. The samples in this study were determined using purposive sampling techniques. This research variable is the skill of writing the procedure text before and after using the modeling technique of class VII SMP Negeri 14 Padang. This research Data is the test score of the text writing procedure before and after using the modeling technique of class VII SMP Negeri 14 Padang. The Data is analyzed with a percentage formula, average count formula, and test-T. The research instrument is a test of work, which is the test of the text of the procedure before and after using the modeling technique. Kata Kunci: Pengaruh, Teknik Pemodelan, Teks Prosedur
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Utami, Mimi Putri, Andria Catri Thamsin, and Mohammad Hafrison. "PENGARUH TEKNIK PEMODELAN TERHADAP KETERAMPILAN MENULIS TEKS PROSEDUR KOMPLEKS SISWA KELAS XI SMKN 1 TAPAN." Pendidikan Bahasa Indonesia 8, no. 1 (March 17, 2019): 121. http://dx.doi.org/10.24036/103925-019883.

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ABSTRACT This article has been writing techniques influence modeling text writing skills against the complex procedure of grade XI 1 — Tapan. This research is quantitative research with quasi experiment method of the one group pretest-postest design. The data in this study is the writing skills test score of complex procedure text grade XI 1 — Tapan before and after using the technique of modeling. The data obtained through test performance. Based on the results of data analysis, researchers concluded three things here; First, complex procedure text writing skills of students of Class XI 1 — Tapan before using the techniques of modelling are at Sufficient qualifications (62.02). Second, complex procedure text writing skills of students of Class XI — 1 using the technique of modeling after the Tapan is at Good qualifications (78.57). Third, based on the t-test, the alternative hypothesis (H1) are accepted on the degrees of confidence and degrees of freedom (dk) = (n-1) because thitung > ttabel (7.10 > 1.70). In other words, modeling techniques to complex procedures text writing skills of students of Class XI 1 — Tapan. Kata kunci: Teknik Pemodelan, Pengaruh, Keterampilan, Teks Prosedur Kompleks
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Rashid, Junaid, Syed Muhammad Adnan Shah, and Aun Irtaza. "Fuzzy topic modeling approach for text mining over short text." Information Processing & Management 56, no. 6 (November 2019): 102060. http://dx.doi.org/10.1016/j.ipm.2019.102060.

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庞, 劲羽. "Polarity-GuidedShort Text Sentiment Analysis Modeling." Instrumentation and Equipments 08, no. 04 (2020): 124–30. http://dx.doi.org/10.12677/iae.2020.84016.

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Strok, Fedor. "Modeling Text Similarity with Parse Thickets." Procedia Computer Science 31 (2014): 1012–21. http://dx.doi.org/10.1016/j.procs.2014.05.354.

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Kino, Yasunobu. "Conceptual Modeling supported by Text Analysis." Procedia Computer Science 126 (2018): 1387–94. http://dx.doi.org/10.1016/j.procs.2018.08.090.

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SHIEBER, STUART M., and RANI NELKEN. "Abbreviated text input using language modeling." Natural Language Engineering 13, no. 2 (July 6, 2006): 165–83. http://dx.doi.org/10.1017/s1351324906004311.

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We address the problem of improving the efficiency of natural language text input under degraded conditions (for instance, on mobile computing devices or by disabled users), by taking advantage of the informational redundancy in natural language. Previous approaches to this problem have been based on the idea of prediction of the text, but these require the user to take overt action to verify or select the system's predictions. We propose taking advantage of the duality between prediction and compression. We allow the user to enter text in compressed form, in particular, using a simple stipulated abbreviation method that reduces characters by 26.4%, yet is simple enough that it can be learned easily and generated relatively fluently. We decode the abbreviated text using a statistical generative model of abbreviation, with a residual word error rate of 3.3%. The chief component of this model is an n-gram language model. Because the system's operation is completely independent from the user's, the overhead from cognitive task switching and attending to the system's actions online is eliminated, opening up the possibility that the compression-based method can achieve text input efficiency improvements where the prediction-based methods have not. We report the results of a user study evaluating this method.
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Dissertations / Theses on the topic "Text modeling"

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Sauper, Christina (Christina Joan). "Content modeling for social media text." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75648.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 129-136).
This thesis focuses on machine learning methods for extracting information from user-generated content. Instances of this data such as product and restaurant reviews have become increasingly valuable and influential in daily decision making. In this work, I consider a range of extraction tasks such as sentiment analysis and aspect-based review aggregation. These tasks have been well studied in the context of newswire documents, but the informal and colloquial nature of social media poses significant new challenges. The key idea behind our approach is to automatically induce the content structure of individual documents given a large, noisy collection of user-generated content. This structure enables us to model the connection between individual documents and effectively aggregate their content. The models I propose demonstrate that content structure can be utilized at both document and phrase level to aid in standard text analysis tasks. At the document level, I capture this idea by joining the original task features with global contextual information. The coupling of the content model and the task-specific model allows the two components to mutually influence each other during learning. At the phrase level, I utilize a generative Bayesian topic model where a set of properties and corresponding attribute tendencies are represented as hidden variables. The model explains how the observed text arises from the latent variables, thereby connecting text fragments with corresponding properties and attributes.
by Christina Sauper.
Ph.D.
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Harrysson, Mattias. "Neural probabilistic topic modeling of short and messy text." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189532.

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Exploring massive amount of user generated data with topics posits a new way to find useful information. The topics are assumed to be “hidden” and must be “uncovered” by statistical methods such as topic modeling. However, the user generated data is typically short and messy e.g. informal chat conversations, heavy use of slang words and “noise” which could be URL’s or other forms of pseudo-text. This type of data is difficult to process for most natural language processing methods, including topic modeling. This thesis attempts to find the approach that objectively give the better topics from short and messy text in a comparative study. The compared approaches are latent Dirichlet allocation (LDA), Re-organized LDA (RO-LDA), Gaussian Mixture Model (GMM) with distributed representation of words, and a new approach based on previous work named Neural Probabilistic Topic Modeling (NPTM). It could only be concluded that NPTM have a tendency to achieve better topics on short and messy text than LDA and RO-LDA. GMM on the other hand could not produce any meaningful results at all. The results are less conclusive since NPTM suffers from long running times which prevented enough samples to be obtained for a statistical test.
Att utforska enorma mängder användargenererad data med ämnen postulerar ett nytt sätt att hitta användbar information. Ämnena antas vara “gömda” och måste “avtäckas” med statistiska metoder såsom ämnesmodellering. Dock är användargenererad data generellt sätt kort och stökig t.ex. informella chattkonversationer, mycket slangord och “brus” som kan vara URL:er eller andra former av pseudo-text. Denna typ av data är svår att bearbeta för de flesta algoritmer i naturligt språk, inklusive ämnesmodellering. Det här arbetet har försökt hitta den metod som objektivt ger dem bättre ämnena ur kort och stökig text i en jämförande studie. De metoder som jämfördes var latent Dirichlet allocation (LDA), Re-organized LDA (RO-LDA), Gaussian Mixture Model (GMM) with distributed representation of words samt en egen metod med namnet Neural Probabilistic Topic Modeling (NPTM) baserat på tidigare arbeten. Den slutsats som kan dras är att NPTM har en tendens att ge bättre ämnen på kort och stökig text jämfört med LDA och RO-LDA. GMM lyckades inte ge några meningsfulla resultat alls. Resultaten är mindre bevisande eftersom NPTM har problem med långa körtider vilket innebär att tillräckligt många stickprov inte kunde erhållas för ett statistiskt test.
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Reynolds, Douglas A. "A Gaussian mixture modeling approach to text-independent speaker identification." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/16903.

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Sad, Hamed. "Text entry interfaces on mobile devices : modeling, design and evaluation." Lorient, 2009. http://www.theses.fr/2009LORIS153.

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This thesis focuses on one of the currently active areas of Human-Computer Interaction (HCI): text entry on mobile devices. Specifically, it deals with the evaluation of text entry method interfaces on mobile devices. We address the two main approach of the evaluation: empirical user testing and model based evaluation. A platform is presented for empirical user tests. The platform aims to shorten the evaluation time and make the evaluation more reproducible and generalizable. It’s implemented on mobile device and equipped with many existing entry methods. It also facilitates the implementation of a new idea for text entry. New ideas can be tested by comparison to the already implemented methods. The platform also includes valuable tools for the evaluation. One tool is provided to help in the creation of a set of test phrases that represent a given language. Another tool is for automating the most common analysis in text entry tests. We presented a framework to identify, classify and model the different operation in the text entry process on mobile devices. It divides the process to two stages: action planning stage and execution stage. The action planning stage studies how the word or the text to be typed is mapped to the physical actions allowed by the entry system used. The execution stage concerns the primitive tasks the user should done to produce text. Some measures are proposed for the evaluation of the two stages. The framework also identifies the different entities that should be taken into account during the evaluation. Model based evaluation requires human performance models for the primitive tasks used in the entry process. We studied three tasks frequently used in the execution stage of the entry process: word disambiguation, word selection from a list, and tilt based interaction. We present an algorithm and design guidelines for the design of efficient ambiguous keyboard layout. A model for the selection of a word from a word-list is constructed from an experimental study. Another model for pointing and scrolling using tilt sensor on a mobile device is also presented. Finally, we present new design directions for the action planning stage that we think more interesting when compared to current designs. They are based on knowledge we already have about our life and about the text we write. They use this knowledge to allow typing a word in a more direct way compared to typing it letter by letter as it’s the case in the current designs. We provide two implementations to help the concretization of the concepts presented in that part of Page 6 Abstract Text entry interfaces on mobile devices: Modeling, design and evaluation, PhD thesis 2009 the thesis. The first shows how our knowledge can be used to type frequent words through using pictographs. The second exploits language syntax to enable the user to type fast and to protect her/him from committing typing errors. The two implementations employ a specially constructed text prediction engine. It implements language redundancy in storage efficient data structures. It introduces the concept of meaning (concept, object, action, relation, etc. ) and separates it from its lexical representation in a given language
Cette thèse concerne la saisie de texte sur les dispositifs mobiles qui est un domaine très actif de l'interaction home-machine (IHM) depuis quelques années. Cette recherche traite plus particulièrement de l'évaluation des méthodes de saisie de texte. Nous abordons les deux approches principales de l'évaluation : l'évaluation expérimentale et l’évaluation par modélisation. Une plateforme pour l'évaluation expérimentale est présentée. Elle vise à faciliter, rendre plus rapide et plus reproductive l'évaluation. Cette plateforme qui intègre de nombreuses méthodes de saisie rend possible leur comparaison et simplifie grandement la conception et le développement d'une nouvelle idée pour la saisie de texte. Enfin, la plateforme inclut des outils pour l'évaluation, comme un outil d'aide à la création d'un corpus de test représentatif de la langue cible ou un outil pour automatiser l'analyse des performances sur la base des métriques standard du domaine. Nous proposons également un cadre (framework) pour décrire, classifier et modéliser les opérations impliquées dans la saisie de texte sur dispositifs mobiles. À la base, nous distinguons deux étapes : la planification et l’exécution. La première étape correspond au processus mental de planification des actions physiques requises pour saisir un mot avec une méthode donnée ; la deuxième étape concerne le processus moteur de production du texte à partir des actions disponibles pour l’utilisateur. On propose, dans cette thèse, des mesures pour une évaluation théorique de ces deux phases de la saisie. L’évaluation théorique s’appuie sur des modèles de la performance humaine pour l’exécution des différentes tâches impliquées dans la saisie. Nous avons étudié en particulier deux tâches fréquemment utilisées dans la phase d'exécution : la sélection d’un mot dans une liste de mots et le pointage et défilement par une interaction basée sur l’inclinaison (tilt). Nous présentons un algorithme et des recommandations pour la conception de claviers ambigus efficaces. Un modèle de performance pour la sélection de mot dans une liste est proposé qui fait suite à une étude expérimentale. Un autre modèle prédit le temps d’exécution du ciblage et du défilement par inclinaison sur un dispositif mobile. Enfin, nous proposons de nouvelles directions originales pour la saisie de texte qui concernent la phase de planification. L’approche exploite notre « connaissance du monde » ainsi que la nature syntaxique des mots du message. Nous nous affranchissons le plus possible d’une saisie de texte « lettre par lettre », pour suivre une approche pictographique où les mots les plus fréquents sont Page 8 Résumé Text entry interfaces on mobile devices: Modeling, design and evaluation, PhD thesis 2009 directement accessibles à partir d’une représentation graphique. L’approche proposée exploite également la syntaxe de la langue pour permettre à l'utilisateur de filtrer gestuellement le mot désiré selon sa catégorie grammaticale. Cette approche pictographique et syntaxique utilise un moteur de prédiction et un codage du lexique spécifiques qui assurent une structure de données efficace et adaptée aux performances limitées des dispositifs mobiles
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Cheng, Chi Wa. "Probabilistic topic modeling and classification probabilistic PCA for text corpora." HKBU Institutional Repository, 2011. http://repository.hkbu.edu.hk/etd_ra/1263.

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Ren, Zhaowei. "Analysis and Modeling of the Structure of Semantic Dynamics in Texts." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1512045439740177.

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Preece, Daniel Joseph. "Text Identification by Example." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2060.pdf.

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Bischof, Jonathan Michael. "Interpretable and Scalable Bayesian Models for Advertising and Text." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11400.

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In the era of "big data", scalable statistical inference is necessary to learn from new and growing sources of quantitative information. However, many commercial and scientific applications also require models to be interpretable to end users in order to generate actionable insights about quantities of interest. We present three case studies of Bayesian hierarchical models that improve the interpretability of existing models while also maintaining or improving the efficiency of inference. The first paper is an application to online advertising that presents an augmented regression model interpretable in terms of the amount of revenue a customer is expected to generate over his or her entire relationship with the company---even if complete histories are never observed. The resulting Poisson Process Regression employs a marginal inference strategy that avoids specifying customer-level latent variables used in previous work that complicate inference and interpretability. The second and third papers are applications to the analysis of text data that propose improved summaries of topic components discovered by these mixture models. While the current practice is to summarize topics in terms of their most frequent words, we show significantly greater interpretability in online experiments with human evaluators by using words that are also relatively exclusive to the topic of interest. In the process we develop a new class of topic models that directly regularize the differential usage of words across topics in order to produce stable estimates of the combined frequency-exclusivity metric as well as proposing efficient and parallelizable MCMC inference strategies.
Statistics
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Foulds, James Richard. "Latent Variable Modeling for Networks and Text| Algorithms, Models and Evaluation Techniques." Thesis, University of California, Irvine, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3631094.

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In the era of the internet, we are connected to an overwhelming abundance of information. As more facets of our lives become digitized, there is a growing need for automatic tools to help us find the content we care about. To tackle the problem of information overload, a standard machine learning approach is to perform dimensionality reduction, transforming complicated high-dimensional data into a manageable, low-dimensional form. Probabilistic latent variable models provide a powerful and elegant framework for performing this transformation in a principled way. This thesis makes several advances for modeling two of the most ubiquitous types of online information: networks and text data.

Our first contribution is to develop a model for social networks as they vary over time. The model recovers latent feature representations of each individual, and tracks these representations as they change dynamically. We also show how to use text information to interpret these latent features.

Continuing the theme of modeling networks and text data, we next build a model of citation networks. The model finds influential scientific articles and the influence relationships between the articles, potentially opening the door for automated exploratory tools for scientists. The increasing prevalence of web-scale data sets provides both an opportunity and a challenge. With more data we can fit more accurate models, as long as our learning algorithms are up to the task. To meet this challenge, we present an algorithm for learning latent Dirichlet allocation topic models quickly, accurately and at scale. The algorithm leverages stochastic techniques, as well as the collapsed representation of the model. We use it to build a topic model on 4.6 million articles from the open encyclopedia Wikipedia in a matter of hours, and on a corpus of 1740 machine learning articles from the NIPS conference in seconds.

Finally, evaluating the predictive performance of topic models is an important yet computationally difficult task. We develop one algorithm for comparing topic models, and another for measuring the progress of learning algorithms for these models. The latter method achieves better estimates than previous algorithms, in many cases with an order of magnitude less computational effort.

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Alsadhan, Majed. "An application of topic modeling algorithms to text analytics in business intelligence." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/17580.

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Master of Science
Department of Computing and Information Sciences
Doina Caragea
William H. Hsu
In this work, we focus on the task of clustering businesses in the state of Kansas based on the content of their websites and their business listing information. Our goal is to cluster the businesses and overcome the challenges facing current approaches such as: data noise, low number of clustered businesses, and lack of evaluation approach. We propose an LSA-based approach to analyze the businesses’ data and cluster those businesses by using Bisecting K-Means algorithm. In this approach, we analyze the businesses’ data by using LSA and produce businesses’ representations in a reduced space. We then use the businesses’ representations to cluster the businesses by applying the Bisecting K-Means algorithm. We also apply an existing LDA-based approach to cluster the businesses and compare the results with our proposed LSA-based approach at the end. In this work, we evaluate the results by using a human-expert-based evaluation procedure. At the end, we visualize the clusters produced in this work by using Google Earth and Tableau. According to our evaluation procedure, the LDA-based approach performed slightly bet- ter then the LSA-based approach. However, with the LDA-based approach, there were some limitations which are: low number of clustered businesses, and not being able to produce a hierarchical tree for the clusters. With the LSA-based approach, we were able to cluster all the businesses and produce a hierarchical tree for the clusters.
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Books on the topic "Text modeling"

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Berry, Michael W. Understanding search engines: Mathematical modeling and text retrieval. 2nd ed. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2006.

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Murray, Browne, ed. Understanding search engines: Mathematical modeling and text retrieval. 2nd ed. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2005.

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Mehler, Alexander, Kai-Uwe Kühnberger, Henning Lobin, Harald Lüngen, Angelika Storrer, and Andreas Witt, eds. Modeling, Learning, and Processing of Text Technological Data Structures. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-22613-7.

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Mehler, Alexander. Modeling, Learning, and Processing of Text Technological Data Structures. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2012.

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Goutsos, Dionysis. Modeling discourse topic: Sequential relations and strategies in expository text. Norwood, N.J: Ablex, 1997.

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Hartmanis, Juris, Jan Leeuwen, and Gerhard Goos. Interactive Multimedia Documents: Modeling, Authoring, and Implementation Experiences. Berlin: Springer-Verlag Berlin Heidelberg, 1999.

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Evans, W. Bryce. Improving your speech: Here's how : voice and diction, basic phonetics, phonics supplement, speech modeling : a basic text with ... exercises. 2nd ed. Dubuque, Iowa: Kendall/Hunt Pub. Co., 1992.

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Aquifer test modeling. Boca Raton, FL: CRC Press, 2007.

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User modelling in text generation. London: Pinter Publishers, 1993.

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Modelling in behavioural ecology: An introductory text. London: Croom Helm, 1986.

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Book chapters on the topic "Text modeling"

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Atkinson-Abutridy, John. "Topic Modeling." In Text Analytics, 165–84. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003280996-8.

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Song, Xiaoge, Yirui Wu, Wenhai Wang, and Tong Lu. "TK-Text: Multi-shaped Scene Text Detection via Instance Segmentation." In MultiMedia Modeling, 201–13. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37734-2_17.

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L. Jockers, Matthew, and Rosamond Thalken. "Topic Modeling." In Text Analysis with R, 211–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39643-5_17.

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Pastor, Moisés, and Francisco Casacuberta. "Pronunciation Modeling." In Text, Speech and Language Technology, 133–48. Dordrecht: Springer Netherlands, 2005. http://dx.doi.org/10.1007/1-4020-2637-4_8.

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Rayar, Frédéric, and Seiichi Uchida. "Comic Text Detection Using Neural Network Approach." In MultiMedia Modeling, 672–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05716-9_60.

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Qin, Zhengcai, Bin Wu, and Meng Li. "Text Image Deblurring via Intensity Extremums Prior." In MultiMedia Modeling, 505–17. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73603-7_41.

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Chen, Jianjun, Hongtao Xie, Yue Hu, and Chenggang Yan. "Uyghur Text Localization with Fast Component Detection." In MultiMedia Modeling, 565–77. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73603-7_46.

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Song, Jiayu, Qinghua Xu, Wei Liu, Yueran Zu, and Mengdong Chen. "Semantic and Morphological Information Guided Chinese Text Classification." In MultiMedia Modeling, 14–26. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37734-2_2.

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Gu, Yang, Veda C. Storey, and Carson C. Woo. "Conceptual Modeling for Financial Investment with Text Mining." In Conceptual Modeling, 528–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25264-3_39.

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Anandarajan, Murugan, Chelsey Hill, and Thomas Nolan. "Cluster Analysis: Modeling Groups in Text." In Practical Text Analytics, 93–115. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95663-3_7.

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Conference papers on the topic "Text modeling"

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Smith, David A., Ryan Cordell, and Elizabeth Maddock Dillon. "Infectious texts: Modeling text reuse in nineteenth-century newspapers." In 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691675.

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ElShal, Sarah, Mithila Mathad, Jaak Simm, Jesse Davis, and Yves Moreau. "Topic modeling of biomedical text." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822606.

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Misra, Hemant, François Yvon, Joemon M. Jose, and Olivier Cappe. "Text segmentation via topic modeling." In Proceeding of the 18th ACM conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1645953.1646170.

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Ichkineeva, Dilara. "TEXT THEME MODELING AS A TOOL FOR ANALYZING TEXT COMPREHENSION." In 4th SGEM International Multidisciplinary Scientific Conferences on SOCIAL SCIENCES and ARTS Proceedings. STEF92 Technology, 2017. http://dx.doi.org/10.5593/sgemsocial2017/32/s14.128.

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Barzilay, Regina. "Probabilistic approaches for modeling text structure and their application to text-to-text generation." In the 12th European Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2009. http://dx.doi.org/10.3115/1610195.1610200.

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"Text Analysis with Ontology Reasoning." In Third International Symposium on Business Modeling and Software Design. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004774100640073.

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Kulekci, M. Oguzhan. "Compressed Context Modeling for Text Compression." In 2011 Data Compression Conference (DCC). IEEE, 2011. http://dx.doi.org/10.1109/dcc.2011.44.

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Fleischmann, Kenneth R., Thomas Clay Templeton, and Jordan Boyd-Graber. "Modeling diverse standpoints in text classification." In the 2011 iConference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1940761.1940863.

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Yao, Enpeng, Guoqing Zheng, Ou Jin, Shenghua Bao, Kailong Chen, Zhong Su, and Yong Yu. "Probabilistic text modeling with orthogonalized topics." In SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2600428.2609471.

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Smith, David A., Ryan Cordel, Elizabeth Maddock Dillon, Nick Stramp, and John Wilkerson. "Detecting and modeling local text reuse." In 2014 IEEE/ACM Joint Conference on Digital Libraries (JCDL). IEEE, 2014. http://dx.doi.org/10.1109/jcdl.2014.6970166.

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Reports on the topic "Text modeling"

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Modlo, Yevhenii O., Serhiy O. Semerikov, Pavlo P. Nechypurenko, Stanislav L. Bondarevskyi, Olena M. Bondarevska, and Stanislav T. Tolmachev. The use of mobile Internet devices in the formation of ICT component of bachelors in electromechanics competency in modeling of technical objects. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3264.

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Computer simulation of technical objects and processes is one of the components of the system of professional training of a modern electromechanics engineer. It has been established that despite the fact that mobile Internet devices (MID) are actively used by electrical engineers, the methods of using them in the process of bachelor in electromechanics training is considered only in some domestic scientific studies. The article highlights the components of the methods of using MID in the formation of the ICT component of the competence of the bachelor in electromechanics in modeling of technical objects, providing for students to acquire basic knowledge in the field of Computer Science and modern ICT and skills to use programming systems, math packages, subroutine libraries, and the like. For processing tabular data, it is proposed to use various freely distributed tools that do not significantly differ in functionality, such as Google Sheets, Microsoft Excel, for processing text data – QuickEdit Text Editor, Google Docs, Microsoft Word. For 3D-modeling and viewing the design and technological documentation, the proposed comprehensive use of Autodesk tools in the training process.
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Mark, David J., Norman W. Scheffner, H. L. Butler, Barry W. Bunch, and Mark S. Dortch. Hydrodynamic and Water Quality Modeling of Lower Green Bay, Wisconsin. Volume 1. Main Text and Appendixes A - E. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada270195.

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Grace, T. M., W. J. Frederick, M. Salcudean, and R. A. Wessel. Black liquor combustion validated recovery boiler modeling: Final year report. Volume 1 (Main text and Appendix I, sections 1--4). Office of Scientific and Technical Information (OSTI), August 1998. http://dx.doi.org/10.2172/296694.

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Intrator, T. Collaboration on Modeling of Ion Bernstein Wave Antenna Array and Coupling to Plasma on Tokamak Fusion Text Reactor. Final report. Office of Scientific and Technical Information (OSTI), June 2000. http://dx.doi.org/10.2172/761048.

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Bilovska, Natalia. TACTICS OF APPROACHING THE AUTHOR CLOSER TO THE READER: INTERACTIVE COOPERATION. Ivan Franko National University of Lviv, February 2022. http://dx.doi.org/10.30970/vjo.2022.51.11408.

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The article clarifies the features of interactive relationships, which are modeled by the addresser of modern media text for maximum impact on the addressee. The author controls the perception of the text, focusing on linguistic competence and an objective picture of the reader’s world. A pragmatic approach to journalistic text makes it possible to identify explicit and implicit forms of dialogue: modeling feedback and interactive settings that can turn a hypothetical reader into a real one, adapting to the addressee’s language thesaurus. Discursive openness to the exchange of views with the addressee leads to the fact that the entire media text becomes a guarantee of commonality of addresser-addressee interpretations. The difference between the addresser and the addressee is minimized, their connection is strengthened through the combination of linguistic consciousness, which, in turn, forms a special structure and semantics of the journalistic text, in which the emphasis is not on I but on the Other. The addressee in some implicit or explicit form is always in all segments of the media text, and the author establishes a trusting relationship with the reader through the phatic linguistic means that the addressee relates to himself. Approaching the addressee is a sign of modern journalistic texts, which show a tendency to dialogue and democratization of forms of mass communication, and their characteristic feature is the actualization in the center of attention of the addressee, latent (mediated by written text) dialogue with which is modeled as real. The addressee in the process of establishing contact with the author of the media text also becomes the part of broad cognitive space. This opportunity is realized if the journalist has different types of competence – communicative and procedural, that is, is able to compare their own thesaurus, their own knowledge with the thesaurus and the picture of the world of his reader. Modern journalism is characterized by the search for contact with the addressee and new effective models of influence and intimacy of relationships that contribute to the creation of a single cognitive space for both, which, in turn, will allow the recipient to move from knowledge to understanding.
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Kiv, Arnold, Serhiy Semerikov, Vladimir Soloviev, Liubov Kibalnyk, Hanna Danylchuk, and Andriy Matviychuk. Experimental Economics and Machine Learning for Prediction of Emergent Economy Dynamics. [б. в.], August 2019. http://dx.doi.org/10.31812/123456789/3209.

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This is an introductory text to a collection of selected papers from the M3E2 2019: The 8th International Conference on Monitoring, Modeling & Management of Emergent Economy, which was held in Odessa, Ukraine, on the May 22-24, 2019. It consists of short introducation and some observations about the event and its future.
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Zelenskyi, Arkadii A. Relevance of research of programs for semantic analysis of texts and review of methods of their realization. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2884.

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One of the main tasks of applied linguistics is the solution of the problem of high-quality automated processing of natural language. The most popular methods for processing natural-language text responses for the purpose of extraction and representation of semantics should be systems that are based on the efficient combination of linguistic analysis technologies and analysis methods. Among the existing methods for analyzing text data, a valid method is used by the method using a vector model. Another effective and relevant means of extracting semantics from the text and its representation is the method of latent semantic analysis (LSA). The LSA method was tested and confirmed its effectiveness in such areas of processing the native language as modeling the conceptual knowledge of the person; information search, the implementation of which LSA shows much better results than conventional vector methods.
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Kiv, Arnold, Pavlo Hryhoruk, Inesa Khvostina, Victoria Solovieva, Vladimir Soloviev, and Serhiy Semerikov. Machine learning of emerging markets in pandemic times. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4122.

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This is an introductory text to a collection of selected papers from the M3E2 2020 Summer: The Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy, which was held in Odessa, Ukraine, on the July 13-18, 2020. It consists of short introduction and some observations about the event and its future.
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Ків, Арнольд Юхимович, Володимир Миколайович Соловйов, Сергій Олексійович Семеріков, Hanna B. Danylchuk, Liubov O. Kibalnyk, Andriy V. Matviychuk, Andrii M. Striuk, et al. Machine learning for prediction of emergent economy dynamics. Криворізький державний педагогічний університет, December 2021. http://dx.doi.org/10.31812/123456789/6973.

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This is an introductory text to a collection of selected papers and revised from the M3E2 2021: 9th International Conference on Monitoring, Modeling & Management of Emergent Economy, which held in Odessa National University of Economics, Odessa, Ukraine, on the May 26-28, 2021. It consists of introduction, conference review and some observations about the event and its future.
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Li, Jian, Peijing Li, and Jingwen Hu. Digital human modeling in automotive engineering applications: a systematic review and bibliometric mapping. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2022. http://dx.doi.org/10.37766/inplasy2022.10.0094.

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Review question / Objective: The aim of this systematic review is to investigate the state of the art of digital human models (DHMs) applied in the field of transportation and automotive engineering, to better inform the development of new models for such use cases. To this end, the proposed systematic review will address the following questions: What is the general trend of research in this field? Which specific use cases, methodologies, and human models are being more widely studied or utilized than others? How can we describe such study characteristics in a structured and quantitative manner? Eligibility criteria: Eligible publications included in the review are screened according to the following criteria: (a) The publication must be a full-text article published in an academic journal or in the proceedings of an academic conference, (b) The publication must be final and the article must be in press, (c) The language of the publication must be English, (d) The publication must apply digital human models in a transportation or automotive engineering context, (e) No particular restrictions are placed on the country and/or region of origin of the publication.
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