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Academic literature on the topic 'Classification de documents Inter-modaux'

Author: Grafiati
Published: 7 September 2023

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Journal articles on the topic "Classification de documents Inter-modaux"

1

Kim, Pan-Jun, and Jae-Yun Lee. "Utilizing Unlabeled Documents in Automatic Classification with Inter-document Similarities." Journal of the Korean Society for information Management 24, no. 1 (2007): 251–71. http://dx.doi.org/10.3743/kosim.2007.24.1.251.

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Puri, Shalini, and Satya Prakash Singh. "A Hybrid Hindi Printed Document Classification System Using SVM and Fuzzy." Journal of Information Technology Research 12, no. 4 (2019): 107–31. http://dx.doi.org/10.4018/jitr.2019100106.

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This article introduces a new advanced tri-layered segmentation and bi-leveled-classifier-based Hindi printed document classification system, which categorizes imaged documents into pre-defined mutually exclusive categories by using SVM and Fuzzy matching at character and document classifications, respectively. During training, the improved and noise-free image is segmented into lines and words by profiling. Then it obtains Shirorekha Less (SL) isolated characters along with upper, left and right modifier components from the SL words. These components use their locations and inter character-modifier component distance to get associate with their corresponding characters only. Further, confidence values of all characters are calculated with SVM training and all characters are mapped into Romanized labels to generate the words. Finally, documents are classified by Fuzzy based matching of Romanized detected words and predefined classes. The average execution times of SL characters are 0.22675 sec. and 0.20375 sec. and classification accuracy are 74.61% and 80.73% for training and testing, respectively.
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Kumari, Lalitha, and Ch Satyanarayana. "An novel cluster based feature selection and document classification model on high dimension trec data." International Journal of Engineering & Technology 7, no. 1.1 (2017): 466. http://dx.doi.org/10.14419/ijet.v7i1.1.10146.

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TREC text documents are complex to analyze the features its relevant similar documents using the traditional document similarity measures. As the size of the TREC repository is increasing, finding relevant clustered documents from a large collection of unstructured documents is a challenging task. Traditional document similarity and classification models are implemented on homogeneous TREC data to find essential features for document entities that are similar to the TREC documents. Also, most of the traditional models are applicable to limited text document sets for text analysis. The main issues in the traditional text mining models in TREC repository include :1) Each document is represented in vector form with many sparsity values 2) Failed to find the document semantic similarity between the intra and inter clusters 3) High mean squared error rate. In this paper, novel feature selection based clustered and classification model is proposed on large number of different TREC repositories. Traditional latent Semantic Indexing and document clustering models are failed to find the topic relevance on large number of TREC clinical text document sets due to computational memory and time. Proposed document feature selection and clustered based classification model is applied on TREC clinical benchmark datasets. From the experimental results, it is proved that the proposed model is efficient than the existing models in terms of computational memory, accuracy and error rate are concerned.
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Dwi P., Galang Amanda, Gregorius Edwadr, and Agus Zainal Arifin. "Pembobotan Berdasarkan Tingkat Kesamaan Semantik pada Metode Fuzzy Semi-Supervised Co-Clustering untuk Pengelompokkan Dokumen Teks." Jurnal ULTIMATICS 6, no. 2 (2014): 46–51. http://dx.doi.org/10.31937/ti.v6i2.333.

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Nowadays, a large number of information can not be reached by the reader because of the misclassification of text-based documents. The misclassified data can also make the readers obtain the wrong information. The method which is proposed by this paper is aiming to classify the documents into the correct group. Each document will have a membership value in several different classes. The method will be used to find the degree of similarity between the two documents is the semantic similarity. In fact, there is no document that doesn’t have a relationship with the other but their relationship might be close to 0. This method calculates the similarity between two documents by taking into account the level of similarity of words and their synonyms. After all inter-document similarity values obtained, a matrix will be created. The matrix is then used as a semi-supervised factor. The output of this method is the value of the membership of each document, which must be one of the greatest membership value for each document which indicates where the documents are grouped. Classification result computed by the method shows a good value which is 90 %.
 Index Terms - Fuzzy co-clustering, Heuristic, Semantica Similiarity, Semi-supervised learning.
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5

Jamnezhad, Mohammad Eiman, and Reza Fattahi. "The comparative study of text documents clustering algorithms." Environment Conservation Journal 16, SE (2015): 133–38. http://dx.doi.org/10.36953/ecj.2015.se1614.

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Clustering is one of the most significant research area in the field of data mining and considered as an important tool in the fast developing information explosion era.Clustering systems are used more and more often in text mining, especially in analyzing texts and to extracting knowledge they contain. Data are grouped into clusters in such a way that the data of the same group are similar and those in other groups are dissimilar. It aims to minimizing intra-class similarity and maximizing inter-class dissimilarity. Clustering is useful to obtain interesting patterns and structures from a large set of data. It can be applied in many areas, namely, DNA analysis, marketing studies, web documents, and classification. This paper aims to study and compare three text documents clustering, namely, k-means, k-medoids, and SOM through F-measure.
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Almagrabi, Hana, Areej Malibari, and John McNaught. "Corpus Analysis and Annotation for Helpful Sentences in Product Reviews." Computer and Information Science 11, no. 2 (2018): 76. http://dx.doi.org/10.5539/cis.v11n2p76.

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For the last two decades, various studies on determining the quality of online product reviews have been concerned with the classification of complete documents into helpful or unhelpful classes using supervised learning methods. As in any supervised machine-learning task, a manually annotated corpus is required to train a model. Corpora annotated for helpful product reviews are an important resource for the understanding of what makes online product reviews helpful and of how to rank them according to their quality. However, most corpora for helpfulness are annotated on the document level: the full review. Little attention has been paid to carrying out a deeper analysis of helpful comments in reviews. In this article, a new annotation scheme is proposed to identify helpful sentences from each product review in the dataset. The annotation scheme, guidelines and the inter-annotator agreement scores are presented and discussed. A high level of inter-annotator agreement is obtained, indicating that the annotated corpus is suitable to support subsequent research.
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7

Jacobsen, Michael. "Doing Business the Chinese Way? On Manadonese Chinese, Entrepreneurship in North Sulawesi." Copenhagen Journal of Asian Studies 24, no. 2 (2006): 105–36. http://dx.doi.org/10.22439/cjas.v24i2.822.

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This article argues and documents that diasporic networking and guanxi relationships in North Sulawesi Province in East Indonesia are not essential for doing business within the Chinese business community. The main argument forwarded is that guanxi governed business networks are but one strategy among several other business strategies employed, when engaging in inter-ethnic and intra-ethnic business transactions. Furthermore, a discussion of the relationship between local Chinese and non-Chinese business environment as well as of the inter-ethnic environment in general constitutes a framework for how to position the Chinese in an overall societal context. Of special interest in this connection are questions of inter-ethnic integration versus assimilation together with questions of descent and ethnic classification in the relation to the surrounding non-Chinese community.
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Belozerov, Vitaly, Natalia Shchitova, and Nikolai Sopnev. "Regulatory and documentary standards of the sustainable development of urban agglomerations in the Russian Federation." InterCarto. InterGIS 27, no. 1 (2021): 17–28. http://dx.doi.org/10.35595/2414-9179-2021-1-27-17-28.

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The article considers the experience of classification documents of the territorial planning and management of urban agglomerations in the Russian Federation. We have analyzed the documents of the federal level the main aim of which is regulating the processes of formation and development of agglomerations in the country. The documents developed in the regions over the past ten years, which regulate the functioning of all Russian urban agglomerations including laws, concepts, strategies, territorial planning schemes, inter-municipal agreements, and regulations on the activities of coordination councils are considered in detail. A comparative analysis of the documents allowed us to group agglomerations according to the degree of representation of the regulatory and documentary basis. There are five groups of agglomerations that differ in the number of documents and the degree of elaboration of agglomeration issues. The results revealed a significant gap between the selected groups. For agglomerations of the first and second groups we have prepared the complete sets of documentation, which reflect sufficiently the main parameters of agglomerations as integral system formations. For agglomerations included in the fourth group, there are no special documents, there are also some relevant materials in the regional documents of strategic and territorial planning which are characterized by poor elaboration. Agglomerations of the fifth group are not provided with regulatory documents at all, they are not considered as special formations. The analysis can contribute to improving the methodology of agglomeration development, understanding the need to expand and improve approaches to the management of urban agglomerations as integral objects. It is obvious that the urgent problem of sustainable development and functioning of urban agglomerations is the need to develop an innovative management model, its coordination with the regulatory framework of regional management structures, and a clear definition of conceptual and terminological and spatial-structural parameters.
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Zhao, Henghui, Wensheng Zhang, Mengxing Huang, Siling Feng, and Yuanyuan Wu. "A Multi-Granularity Heterogeneous Graph for Extractive Text Summarization." Electronics 12, no. 10 (2023): 2184. http://dx.doi.org/10.3390/electronics12102184.

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Extractive text summarization selects the most important sentences from a document, preserves their original meaning, and produces an objective and fact-based summary. It is faster and less computationally intensive than abstract summarization techniques. Learning cross-sentence relationships is crucial for extractive text summarization. However, most of the language models currently in use process text data sequentially, which makes it difficult to capture such inter-sentence relations, especially in long documents. This paper proposes an extractive summarization model based on the graph neural network (GNN) to address this problem. The model effectively represents cross-sentence relationships using a graph-structured document representation. In addition to sentence nodes, we introduce two nodes with different granularity in the graph structure, words and topics, which bring different levels of semantic information. The node representations are updated by the graph attention network (GAT). The final summary is obtained using the binary classification of the sentence nodes. Our text summarization method was demonstrated to be highly effective, as supported by the results of our experiments on the CNN/DM and NYT datasets. To be specific, our approach outperformed baseline models of the same type in terms of ROUGE scores on both datasets, indicating the potential of our proposed model for enhancing text summarization tasks.
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Solomonovich, Nadav, and Ruth Kark. "Land Privatization in Nineteenth-century Ottoman Palestine." Islamic Law and Society 22, no. 3 (2015): 221–52. http://dx.doi.org/10.1163/15685195-00223p02.

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This article examines land privatization in late nineteenth-century Ottoman Palestine through the extension of possession in miri lands, on the one hand, and its transformation into fee-simple property through change in land category classification (i.e., miri to mülk), on the other. Using primary sources, particularly Ottoman documents and correspondence of the German Consulate in Jerusalem, we analyze this process, as reflected in several cases involving foreign subjects and Ottoman authorities. We argue that privatization began as informal violations of the law, proceeded with the struggle of landholders against authorities who tried to reverse the process, and ended in victory for the landholders after the state ceded to their demands, inter alia, as a result of pressure from foreign nations and their consuls. Thus did de facto land privatization become de jure privatization.
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