Relevant bibliographies by topics / Automated Text Categorization

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Automated Text Categorization'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Automated Text Categorization"

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Sebastiani, Fabrizio. "Machine learning in automated text categorization." ACM Computing Surveys 34, no. 1 (March 2002): 1–47. http://dx.doi.org/10.1145/505282.505283.

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Apté, Chidanand, Fred Damerau, and Sholom M. Weiss. "Automated learning of decision rules for text categorization." ACM Transactions on Information Systems 12, no. 3 (July 1994): 233–51. http://dx.doi.org/10.1145/183422.183423.

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Pushpa, M. "Semi Automated Text Categorization using Demonstration Based Term Set." International Journal of Computer Science, Engineering and Applications 2, no. 4 (August 31, 2012): 71–77. http://dx.doi.org/10.5121/ijcsea.2012.2408.

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Tellez, Eric S., Daniela Moctezuma, Sabino Miranda-Jiménez, and Mario Graff. "An automated text categorization framework based on hyperparameter optimization." Knowledge-Based Systems 149 (June 2018): 110–23. http://dx.doi.org/10.1016/j.knosys.2018.03.003.

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Prajapati, Bhagirath, Sanjay Garg, and N. C Chauhan. "Some Investigations on Machine Learning Techniques for Automated Text Categorization." International Journal of Computer Applications 71, no. 3 (June 26, 2013): 32–36. http://dx.doi.org/10.5120/12340-8617.

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Jia, Longjia, Tieli Sun, Fengqin Yang, Hongguang Sun, Bangzuo Zhang, and Chih-Cheng Hung. "A New Category-Based Weighting Scheme for Automated Text Categorization." Journal of Computational and Theoretical Nanoscience 12, no. 12 (December 1, 2015): 5198–205. http://dx.doi.org/10.1166/jctn.2015.4500.

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Radhi, Abdul Kareem M. "CONSTRUCTION OF AUTOMATED SYSTEM FOR INFORMATION EXTRACTION AND TEXT CATEGORIZATION." Journal of Al-Nahrain University Science 11, no. 3 (December 1, 2008): 156–74. http://dx.doi.org/10.22401/jnus.11.3.20.

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Endalie, Demeke, and Getamesay Haile. "Automated Amharic News Categorization Using Deep Learning Models." Computational Intelligence and Neuroscience 2021 (July 27, 2021): 1–9. http://dx.doi.org/10.1155/2021/3774607.

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For decades, machine learning techniques have been used to process Amharic texts. The potential application of deep learning on Amharic document classification has not been exploited due to a lack of language resources. In this paper, we present a deep learning model for Amharic news document classification. The proposed model uses fastText to generate text vectors to represent semantic meaning of texts and solve the problem of traditional methods. The text vectors matrix is then fed into the embedding layer of a convolutional neural network (CNN), which automatically extracts features. We conduct experiments on a data set with six news categories, and our approach produced a classification accuracy of 93.79%. We compared our method to well-known machine learning algorithms such as support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), XGBoost (XGB), and random forest (RF) and achieved good results.
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Wang, Dali, Ying Bai, and David Hamblin. "A Hybrid Learning Algorithm in Automated Text Categorization of Legacy Data." International Journal of Artificial Intelligence & Applications 10, no. 5 (September 30, 2019): 39–47. http://dx.doi.org/10.5121/ijaia.2019.10504.

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De Souza, Alberto F., Felipe Pedroni, Elias Oliveira, Patrick M. Ciarelli, Wallace Favoreto Henrique, Lucas Veronese, and Claudine Badue. "Automated multi-label text categorization with VG-RAM weightless neural networks." Neurocomputing 72, no. 10-12 (June 2009): 2209–17. http://dx.doi.org/10.1016/j.neucom.2008.06.028.

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Dissertations / Theses on the topic "Automated Text Categorization"

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Wirantono, Marcel. "Automated text categorization with collaboratively tagged data." Thesis, University of Ottawa (Canada), 2009. http://hdl.handle.net/10393/28116.

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Recent popularity of collaborative tagging as a component of a retrieval system has lead us to study such a system. Similar to text categorization, albeit in a less centralized fashion, collaborative tagging relies on humans to annotate documents with metadata descriptions, i.e. tags. For that reason, this thesis attempts to extend the tagging process to include a more consistent non-human annotations in the form of automatic text categorization. In applying automatic text categorization to collaboratively tagged data, we have created two sets of experiment. The first experiment compares two classification methods, Naive Bayes and Support Vector Machine (SVM) in a straightforward 1-vs. all classification. The results of the comparison allow us to make important observations such as the benefit of using a maximum margin classifiers (SVM) in annotating concepts with skewed document distributions as well as establishing a baseline result. For the second experiment, we have found that the lack of structure in tagging has limited our learning approach to the simple 1-vs. all setting. Inspired by the application of hierarchical categorization in web directories[15], we introduce in our second experiment a categorization approach that automatically builds a hierarchy from the tag space and incorporates it to the training and classification process. Unlike previous hierarchical categorizations that rely on human-generated hierarchies, our hierarchical approach relies on an artificial hierarchy that is created from tag usage analysis. After the method was applied to the dataset, we compared the result of the new methods with the baseline results from the first experiment. Based on that comparison, we observed that our hierarchical approach improves not only on the quality of predictions, but also the efficiency (total training and classification time) of our automatic text categorization system.
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Eramo, Mark D. Sutter Christopher M. "Automated psychological categorization via linguistic processing system /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Sep%5FEramo.pdf.

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Thesis (M.S. in Information Technology Management and M.S. in Information Systems and Operations)--Naval Postgraduate School, Sept. 2004.
Thesis advisor(s): Raymond Buettner, Magdi Kamel. Includes bibliographical references (p. 115-122). Also available online.
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Sutter, Christopher M., and Mark D. Eramo. "Automated psychological categorization via linguistic processing system." Thesis, Monterey, California. Naval Postgraduate School, 2004. http://hdl.handle.net/10945/1439.

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Approved for public release; distribution is unlimited
Influencing one's adversary has always been an objective in warfare. However, to date the majority of influence operations have been geared toward the masses or to very small numbers of individuals. Although marginally effective, this approach is inadequate with respect to larger numbers of high value targets and to specific subsets of the population. Limited human resources have prevented a more tailored approach, which would focus on segmentation, because individual targeting demands significant time from psychological analysts. This research examined whether or not Information Technology (IT) tools, specializing in text mining, are robust enough to automate the categorization/segmentation of individual profiles for the purpose of psychological operations (PSYOP). Research indicated that only a handful of software applications claimed to provide adequate functionality to perform these tasks. Text mining via neural networks was determined to be the best approach given the constraints of the profile data and the desired output. Five software applications were tested and evaluated for their ability to reproduce the results of a social psychologist. Through statistical analysis, it was concluded that the tested applications are not currently mature enough to produce accurate results that would enable automated segmentation of individual profiles based on supervised linguistic processing.
Captain, United States Marine Corps
Lieutenant, United States Navy
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SOARES, FABIO DE AZEVEDO. "AUTOMATIC TEXT CATEGORIZATION BASED ON TEXT MINING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2013. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=23213@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
A Categorização de Documentos, uma das tarefas desempenhadas em Mineração de Textos, pode ser descrita como a obtenção de uma função que seja capaz de atribuir a um documento uma categoria a que ele pertença. O principal objetivo de se construir uma taxonomia de documentos é tornar mais fácil a obtenção de informação relevante. Porém, a implementação e a execução de um processo de Categorização de Documentos não é uma tarefa trivial: as ferramentas de Mineração de Textos estão em processo de amadurecimento e ainda, demandam elevado conhecimento técnico para a sua utilização. Além disso, exercendo grande importância em um processo de Mineração de Textos, a linguagem em que os documentos se encontram escritas deve ser tratada com as particularidades do idioma. Contudo há grande carência de ferramentas que forneçam tratamento adequado ao Português do Brasil. Dessa forma, os objetivos principais deste trabalho são pesquisar, propor, implementar e avaliar um framework de Mineração de Textos para a Categorização Automática de Documentos, capaz de auxiliar a execução do processo de descoberta de conhecimento e que ofereça processamento linguístico para o Português do Brasil.
Text Categorization, one of the tasks performed in Text Mining, can be described as the achievement of a function that is able to assign a document to the category, previously defined, to which it belongs. The main goal of building a taxonomy of documents is to make easier obtaining relevant information. However, the implementation and execution of Text Categorization is not a trivial task: Text Mining tools are under development and still require high technical expertise to be handled, also having great significance in a Text Mining process, the language of the documents should be treated with the peculiarities of each idiom. Yet there is great need for tools that provide proper handling to Portuguese of Brazil. Thus, the main aims of this work are to research, propose, implement and evaluate a Text Mining Framework for Automatic Text Categorization, capable of assisting the execution of knowledge discovery process and provides language processing for Brazilian Portuguese.
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Hall, Scott R. "Automatic text categorization applied to E-mail." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FHall.pdf.

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Demirtas, Kezban. "Automatic Video Categorization And Summarization." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611113/index.pdf.

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In this thesis, we make automatic video categorization and summarization by using subtitles of videos. We propose two methods for video categorization. The first method makes unsupervised categorization by applying natural language processing techniques on video subtitles and uses the WordNet lexical database and WordNet domains. The method starts with text preprocessing. Then a keyword extraction algorithm and a word sense disambiguation method are applied. The WordNet domains that correspond to the correct senses of keywords are extracted. Video is assigned a category label based on the extracted domains. The second method has the same steps for extracting WordNet domains of video but makes categorization by using a learning module. Experiments with documentary videos give promising results in discovering the correct categories of videos. Video summarization algorithms present condensed versions of a full length video by identifying the most significant parts of the video. We propose a video summarization method using the subtitles of videos and text summarization techniques. We identify significant sentences in the subtitles of a video by using text summarization techniques and then we compose a video summary by finding the video parts corresponding to these summary sentences.
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Eklund, Johan. "With or without context : Automatic text categorization using semantic kernels." Doctoral thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-8949.

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In this thesis text categorization is investigated in four dimensions of analysis: theoretically as well as empirically, and as a manual as well as a machine-based process. In the first four chapters we look at the theoretical foundation of subject classification of text documents, with a certain focus on classification as a procedure for organizing documents in libraries. A working hypothesis used in the theoretical analysis is that classification of documents is a process that involves translations between statements in different languages, both natural and artificial. We further investigate the close relationships between structures in classification languages and the order relations and topological structures that arise from classification. A classification algorithm that gets a special focus in the subsequent chapters is the support vector machine (SVM), which in its original formulation is a binary classifier in linear vector spaces, but has been extended to handle classification problems for which the categories are not linearly separable. To this end the algorithm utilizes a category of functions called kernels, which induce feature spaces by means of high-dimensional and often non-linear maps. For the empirical part of this study we investigate the classification performance of semantic kernels generated by different measures of semantic similarity. One category of such measures is based on the latent semantic analysis and the random indexing methods, which generates term vectors by using co-occurrence data from text collections. Another semantic measure used in this study is pointwise mutual information. In addition to the empirical study of semantic kernels we also investigate the performance of a term weighting scheme called divergence from randomness, that has hitherto received little attention within the area of automatic text categorization. The result of the empirical part of this study shows that the semantic kernels generally outperform the “standard” (non-semantic) linear kernel, especially for small training sets. A conclusion that can be drawn with respect to the investigated datasets is therefore that semantic information in the kernel in general improves its classification performance, and that the difference between the standard kernel and the semantic kernels is particularly large for small training sets. Another clear trend in the result is that the divergence from randomness weighting scheme yields a classification performance surpassing that of the common tf-idf weighting scheme.
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Borggren, Lukas. "Automatic Categorization of News Articles With Contextualized Language Models." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177004.

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This thesis investigates how pre-trained contextualized language models can be adapted for multi-label text classification of Swedish news articles. Various classifiers are built on pre-trained BERT and ELECTRA models, exploring global and local classifier approaches. Furthermore, the effects of domain specialization, using additional metadata features and model compression are investigated. Several hundred thousand news articles are gathered to create unlabeled and labeled datasets for pre-training and fine-tuning, respectively. The findings show that a local classifier approach is superior to a global classifier approach and that BERT outperforms ELECTRA significantly. Notably, a baseline classifier built on SVMs yields competitive performance. The effect of further in-domain pre-training varies; ELECTRA’s performance improves while BERT’s is largely unaffected. It is found that utilizing metadata features in combination with text representations improves performance. Both BERT and ELECTRA exhibit robustness to quantization and pruning, allowing model sizes to be cut in half without any performance loss.
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Zhang, Xueying. "Rough set theory based automatic text categorization and the handling of semantic heterogeneity." Bonn Informationszentrum Sozialwiss, 2006. http://deposit.ddb.de/cgi-bin/dokserv?id=2704442&prov=M&dokv̲ar=1&doke̲xt=htm.

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Pereira, Dennis V. "Automatic Lexicon Generation for Unsupervised Part-of-Speech Tagging Using Only Unannotated Text." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/10094.

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With the growing number of textual resources available, the ability to understand them becomes critical. An essential first step in understanding these sources is the ability to identify the parts-of-speech in each sentence. The goal of this research is to propose, improve, and implement an algorithm capable of finding terms (words in a corpus) that are used in similar ways--a term categorizer. Such a term categorizer can be used to find a particular part-of-speech, i.e. nouns in a corpus, and generate a lexicon. The proposed work is not dependent on any external sources of information, such as dictionaries, and it shows a significant improvement (~30%) over an existing method of categorization. More importantly, the proposed algorithm can be applied as a component of an unsupervised part-of-speech tagger, making it truly unsupervised, requiring only unannotated text. The algorithm is discussed in detail, along with its background, and its performance. Experimentation shows that the proposed algorithm performs within 3% of the baseline, the Penn-TreeBank Lexicon.
Master of Science
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Books on the topic "Automated Text Categorization"

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Automatic Text Categorization Applied to E-Mail. Storming Media, 2002.

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Book chapters on the topic "Automated Text Categorization"

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Gómez, José María, José Carlos Cortizo, Enrique Puertas, and Miguel Ruiz. "Concept Indexing for Automated Text Categorization." In Natural Language Processing and Information Systems, 195–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27779-8_17.

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Debole, Franca, and Fabrizio Sebastiani. "Supervised Term Weighting for Automated Text Categorization." In Text Mining and its Applications, 81–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-45219-5_7.

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Embrechts, Mark J., Jonathan Linton, Walter F. Bogaerts, Bram Heyns, and Paul Evangelista. "Automated Text Categorization Based on Readability Fingerprints." In Lecture Notes in Computer Science, 408–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74695-9_42.

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Ferilli, Stefano, Nicola Fanizzi, and Giovanni Semeraro. "Learning Logic Models for Automated Text Categorization." In AI*IA 2001: Advances in Artificial Intelligence, 81–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45411-x_10.

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Apté, Chidanand, Fred Damerau, and Sholom M. Weiss. "Towards Language Independent Automated Learning of Text Categorization Models." In SIGIR ’94, 23–30. London: Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-2099-5_3.

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Gómez Hidalgo, José María, Manuel de Buenaga Rodríguez, and José Carlos Cortizo Pérez. "The Role of Word Sense Disambiguation in Automated Text Categorization." In Natural Language Processing and Information Systems, 298–309. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428817_27.

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Manomaisupat, Pensiri, Bogdan Vrusias, and Khurshid Ahmad. "Categorization of Large Text Collections: Feature Selection for Training Neural Networks." In Intelligent Data Engineering and Automated Learning – IDEAL 2006, 1003–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11875581_120.

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Giorgetti, Daniela, Irina Prodanof, and Fabrizio Sebastiani. "Mapping an Automated Survey Coding Task into a Probabilistic Text Categorization Framework." In Advances in Natural Language Processing, 115–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45433-0_18.

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Qiang, Wang, Wang XiaoLong, and Guan Yi. "A Study of Semi-discrete Matrix Decomposition for LSI in Automated Text Categorization." In Natural Language Processing – IJCNLP 2004, 606–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30211-7_64.

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Galavotti, Luigi, Fabrizio Sebastiani, and Maria Simi. "Experiments on the Use of Feature Selection and Negative Evidence in Automated Text Categorization." In Research and Advanced Technology for Digital Libraries, 59–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45268-0_6.

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Conference papers on the topic "Automated Text Categorization"

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Patel, Atul, Samprati Pathak, and Md Irfan Khan. "Automated Text Categorization." In 2021 3rd International Conference on Signal Processing and Communication (ICPSC). IEEE, 2021. http://dx.doi.org/10.1109/icspc51351.2021.9451670.

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Yasotha, R., and E. Y. A. Charles. "Automated text document categorization." In 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS). IEEE, 2015. http://dx.doi.org/10.1109/intelcis.2015.7397271.

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Tan, Jiaqi, Wenye Li, and Haoming Li. "Automated text categorization by generalized kernel machines." In 2014 IEEE International Conference on Information and Automation (ICIA). IEEE, 2014. http://dx.doi.org/10.1109/icinfa.2014.6932685.

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Debole, Franca, and Fabrizio Sebastiani. "Supervised term weighting for automated text categorization." In the 2003 ACM symposium. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/952532.952688.

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Giorgetti, Daniela, and Fabrizio Sebastiani. "Multiclass text categorization for automated survey coding." In the 2003 ACM symposium. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/952532.952691.

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Liu, Ying, and Han Tong Loh. "Domain concept handling in automated text categorization." In 2010 5th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2010. http://dx.doi.org/10.1109/iciea.2010.5514692.

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Xu, Hongzhi, and Chunping Li. "A Novel Term Weighting Scheme for Automated Text Categorization." In Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007). IEEE, 2007. http://dx.doi.org/10.1109/isda.2007.26.

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Xu, Hongzhi, and Chunping Li. "A Novel Term Weighting Scheme for Automated Text Categorization." In Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007). IEEE, 2007. http://dx.doi.org/10.1109/isda.2007.4389699.

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Machhour, Hamid, and Ismail Kassou. "Improving text categorization: A fully automated ontology based approach." In 2013 International Conference on Communications and Information Technology (ICCIT). IEEE, 2013. http://dx.doi.org/10.1109/iccitechnology.2013.6579524.

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Wang, Dali, Ying Bai, and David Hamblin. "A Machine Learning Algorithm in Automated Text Categorization of Legacy Archives." In 8th International Conference on Soft Computing, Artificial Intelligence and Applications. Aircc Publishing Corporation, 2019. http://dx.doi.org/10.5121/csit.2019.90701.

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