Relevant bibliographies by topics / Analysis of Text Classification

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Analysis of Text Classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Analysis of Text Classification"

1

Khan, Nida Zafar, and Prof S. R. Yadav. "Analysis of Text Classification Algorithms: A Review." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (2019): 579–81. http://dx.doi.org/10.31142/ijtsrd21448.

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Sudheep Elayidom, M., Chinchu Jose, Anitta Puthussery, and Neenu K Sasi. "Text Classification for Authorship Attribution Analysis." Advanced Computing: An International Journal 4, no. 5 (2013): 1–10. http://dx.doi.org/10.5121/acij.2013.4501.

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Emery, Peter G. "Text Classification and Text Analysis in Advances Translation Teaching." Meta 36, no. 4 (2002): 567–77. http://dx.doi.org/10.7202/002707ar.

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Résumé On étudie d'abord les différentes bases théoriques sur lesquelles se fonde la classification des textes tout en préconisant comme critère prépondérant le domaine ou « contexte social ». On traite des méthodes d'analyse de textes en tenant compte de certaines théories linguistiques. Enfin, on souligne l'importance de l'analyse textuelle dans la pédagogie de la traduction. Les exemples illuslrafifs sont tirés de traductions arabe/anglais.
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Arora, Jhanvi, and Santosh Kumar Bharti. "Rhetorical Analysis and Classification of Poem Text." International Journal of Semiotics and Visual Rhetoric 5, no. 1 (2021): 57–71. http://dx.doi.org/10.4018/ijsvr.2021010105.

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Poetry is one of the richest forms of literature, which in itself includes all components of language a human learns; by components here, the context is towards the rhetorical devices. The rhetorical devices constitute the witty use of words used in the reference to things. The work intends to identify the forms of creative references used by the poets to contrast their style of writing and categorize the text on the basis of the same. On the basis of each such prominent device such as rhymes or alliteration, one can derive the boundary or similarity percentage amongst the poems, which can be
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Kim, Jiyun, and Han-joon Kim. "Multidimensional Text Warehousing for Automated Text Classification." Journal of Information Technology Research 11, no. 2 (2018): 168–83. http://dx.doi.org/10.4018/jitr.2018040110.

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This article describes how, in the era of big data, a data warehouse is an integrated multidimensional database that provides the basis for the decision making required to establish crucial business strategies. Efficient, effective analysis requires a data organization system that integrates and manages data of various dimensions. However, conventional data warehousing techniques do not consider the various data manipulation operations required for data-mining activities. With the current explosion of text data, much research has examined text (or document) repositories to support text mining
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Minaee, Shervin, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. "Deep Learning--based Text Classification." ACM Computing Surveys 54, no. 3 (2021): 1–40. http://dx.doi.org/10.1145/3439726.

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Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this article, we provide a comprehensive review of more than 150 deep learning--based models for text classification developed in recent years, and we discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the pe
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Zuo, Wan Li, Zhi Yan Wang, Ning Ma, and Hong Liang. "Study on Consistency Analysis in Text Categorization." Applied Mechanics and Materials 539 (July 2014): 181–84. http://dx.doi.org/10.4028/www.scientific.net/amm.539.181.

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Accurate classification of text is a basic premise of extracting various types of information on the Web efficiently and utilizing the network resources properly. In this paper, a brand new text classification method was proposed. Consistency analysis method is a type of iterative algorithm, which mainly trains different classifiers (weak classifier) by aiming at the same training set, and then these classifiers will be gathered for testing the consistency degrees of various classification methods for the same text, thus to manifest the knowledge of each type of classifier. It main determines
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BUSAGALA, L. S. P., W. OHYAMA, T. WAKABAYASHI, and F. KIMURA. "Improving Automatic Text Classification by Integrated Feature Analysis." IEICE Transactions on Information and Systems E91-D, no. 4 (2008): 1101–9. http://dx.doi.org/10.1093/ietisy/e91-d.4.1101.

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SzymańSki, Julian. "Comparative Analysis of Text Representation Methods Using Classification." Cybernetics and Systems 45, no. 2 (2014): 180–99. http://dx.doi.org/10.1080/01969722.2014.874828.

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AbuZeina, Dia, and Fawaz S. Al-Anzi. "Employing fisher discriminant analysis for Arabic text classification." Computers & Electrical Engineering 66 (February 2018): 474–86. http://dx.doi.org/10.1016/j.compeleceng.2017.11.002.

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Dissertations / Theses on the topic "Analysis of Text Classification"

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Boulton, David. "Fine art image classification based on text analysis." Thesis, University of Surrey, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252478.

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Stein, Roger Alan. "An analysis of hierarchical text classification using word embeddings." Universidade do Vale do Rio dos Sinos, 2018. http://www.repositorio.jesuita.org.br/handle/UNISINOS/7624.

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Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2019-03-07T14:41:05Z No. of bitstreams: 1 Roger Alan Stein_.pdf: 476239 bytes, checksum: a87a32ffe84d0e5d7a882e0db7b03847 (MD5)<br>Made available in DSpace on 2019-03-07T14:41:05Z (GMT). No. of bitstreams: 1 Roger Alan Stein_.pdf: 476239 bytes, checksum: a87a32ffe84d0e5d7a882e0db7b03847 (MD5) Previous issue date: 2018-03-28<br>CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Efficient distributed numerical word representation models (word embeddings) combined with modern machine learning algorithms have recently
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Roa, Diego. "Analysis of Short Text Classification strategies using Out of-domain Vocabularies." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237530.

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Short text classification has become an important task for the Natural Language Processing (NLP) community due to the rapidly growing amount of tweets, search queries, short reviews and descriptions in different contexts such as e-commerce, social media and internal Enterprise Resource Planning (ERP) systems. The brevity and sparsity of such text data represent challenges to build accurate classification models. In order to overcome these challenges, most of the approaches proposed in existing literature rely on the use of implicit or explicit text representations, but the effect of using such
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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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Subramanian, Nandita. "Analysis of Rank Distance for Malware Classification." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479823187035784.

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Pizzol, Martino. "Entity extraction e text classification come strumenti per la sentiment analysis di testi giornalistici." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8396/.

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In this thesis we are going to talk about technologies which allow us to approach sentiment analysis on newspapers articles. The final goal of this work is to help social scholars to do content analysis on big corpora of texts in a faster way thanks to the support of automatic text classification.
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Norlund, Tobias. "The Use of Distributional Semantics in Text Classification Models : Comparative performance analysis of popular word embeddings." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-127991.

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In the field of Natural Language Processing, supervised machine learning is commonly used to solve classification tasks such as sentiment analysis and text categorization. The classical way of representing the text has been to use the well known Bag-Of-Words representation. However lately low-dimensional dense word vectors have come to dominate the input to state-of-the-art models. While few studies have made a fair comparison of the models' sensibility to the text representation, this thesis tries to fill that gap. We especially seek insight in the impact various unsupervised pre-trained vect
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RezaeiDivkolaei, Pouya. "DETECTION, CLASSIFICATION, AND LOCATION IDENTIFICATION OF TRAFFIC CONGESTION FROM TWITTER STREAM ANALYSIS." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2257.

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Social media today is an important source of information about various events happening around the world. Among various social networking platforms, microtext based ones such as Twitter are of special interest as they are also a rich source of real-time events. In this thesis, our goal is to study the effectiveness of using Twitter as a social sensor for obtaining real-time information on road traffic conditions. Specifically, we focus on: i) identifying tweets that contain traffic event related information, ii) classify such tweets into six main groups of accident, fire, road construction, po
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Wang, Wenbo. "Automatic Emotion Identification from Text." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1440974400.

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Boynukalin, Zeynep. "Emotion Analysis Of Turkish Texts By Using Machine Learning Methods." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614521/index.pdf.

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Automatically analysing the emotion in texts is in increasing interest in today&rsquo<br>s research fields. The aim is to develop a machine that can detect type of user&rsquo<br>s emotion from his/her text. Emotion classification of English texts is studied by several researchers and promising results are achieved. In this thesis, an emotion classification study on Turkish texts is introduced. To the best of our knowledge, this is the first study on emotion analysis of Turkish texts. In English there exists some well-defined datasets for the purpose of emotion classification, but we could not
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Books on the topic "Analysis of Text Classification"

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Spray, Judith A. Multiple-category classification using a sequential probability ratio test. American College Testing Program, 1993.

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Spray, Judith A. Multiple-category classification using a sequential probability ratio test. American College Testing Program, 1993.

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Spray, Judith A. Multiple-category classification using a sequential probability ratio test. American College Testing Program, 1993.

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Genome clustering: From linguistic models to classification of genetic texts. Springer, 2010.

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Almodóvar, Antonio Rodríguez. El texto infinito: Ensayos sobre el cuento popular. Fundación Germán Sánchez Ruipérez, 2004.

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Anderson, Gregory L. Classification analysis of vibration data from SH-60B Helicopter Transmission Test Facility. Naval Postgraduate School, 1997.

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Rovenstine, Michael J. Classification analysis of vibration data from SH-60B Helicopter Transmission Test Facility. Naval Postgraduate School, 1997.

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Los cuentos populares o la tentativa de un texto infinito. Universidad de Murcia, 1989.

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Lin, Chuan-Ju. Effects of item-selection criteria on classification testing with the sequential probability ratio test. ACT, Inc., 2000.

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Lin, Chuan-Ju. Effects of item-selection criteria on classification testing with the sequential probability ratio test. ACT, Inc., 2000.

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Book chapters on the topic "Analysis of Text Classification"

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L. Jockers, Matthew, and Rosamond Thalken. "Classification." In Text Analysis with R. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39643-5_16.

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Sarkar, Dipanjan. "Text Classification." In Text Analytics with Python. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2388-8_4.

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Sarkar, Dipanjan. "Text Classification." In Text Analytics with Python. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4354-1_5.

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Klein, Harald. "Classification of Text Analysis Software." In Classification and Knowledge Organization. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59051-1_37.

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Popping, R., and C. W. Roberts. "Network Approaches in Text Analysis." In Classification and Knowledge Organization. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59051-1_40.

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Gerard, Charlie. "Text classification and sentiment analysis." In Practical Machine Learning in JavaScript. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6418-8_4.

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Felaco, Cristiano, and Anna Parola. "Studying Narrative Flows by Text Analysis and Network Text Analysis." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52680-1_3.

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Jockers, Matthew L. "Classification." In Text Analysis with R for Students of Literature. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03164-4_12.

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Hvitfeldt, Emil, and Julia Silge. "Classification." In Supervised Machine Learning for Text Analysis in R. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003093459-10.

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Kralicek, Jiri, and Jiri Matas. "Fast Text vs. Non-text Classification of Images." In Document Analysis and Recognition – ICDAR 2021. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86337-1_2.

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Conference papers on the topic "Analysis of Text Classification"

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Thi Xuan Lam, Thanh, Anh Duc Le, and Masaki Nakagawa. "User Interface for Text and Non-Text Classification." In 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW). IEEE, 2019. http://dx.doi.org/10.1109/icdarw.2019.20044.

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Zhao, Miao, Rui-Qi Wang, Fei Yin, Xu-Yao Zhang, Lin-Lin Huang, and Jean-Marc Ogier. "Fast Text/non-Text Image Classification with Knowledge Distillation." In 2019 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2019. http://dx.doi.org/10.1109/icdar.2019.00234.

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Gallo, Ignazio, Shah Nawaz, and Alessandro Calefati. "Semantic Text Encoding for Text Classification Using Convolutional Neural Networks." In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2017. http://dx.doi.org/10.1109/icdar.2017.323.

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Wu, Qin, Eddie Fuller, and Cun-Quan Zhang. "Text Document Classification and Pattern Recognition." In 2009 International Conference on Advances in Social Network Analysis and Mining (ASONAM). IEEE, 2009. http://dx.doi.org/10.1109/asonam.2009.21.

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Luo, Xi, Wataru Ohyama, Tetsushi Wakabayashi, and Fumitaka Kimura. "A Study on Automatic Chinese Text Classification." In 2011 International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2011. http://dx.doi.org/10.1109/icdar.2011.187.

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Zha, Shengxin, Xujun Peng, Huaigu Cao, Xiandan Zhuang, Pradeep Natarajan, and Prem Natarajan. "Text Classification via iVector Based Feature Representation." In 2014 11th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, 2014. http://dx.doi.org/10.1109/das.2014.10.

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Nayef, Nibal, and Jean-Marc Ogier. "Text zone classification using unsupervised feature learning." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333867.

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Deng, Zhenrong, Baojun Zhang, and Jingjing Zhang. "Mixed feature expansion-based short text classification." In 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA). IEEE, 2018. http://dx.doi.org/10.1109/icbda.2018.8367705.

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Matsushita, Tomohisa, Cheng Cheng, Yujiro Murata, Bilan Zhu, and Masaki Nakagawa. "Effect of Text/Non-text Classification for Ink Search Employing String Recognition." In 2012 10th IAPR International Workshop on Document Analysis Systems (DAS). IEEE, 2012. http://dx.doi.org/10.1109/das.2012.33.

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Escalante, Hugo Jair, Manuel Montes y Gomez, Luis Villasenor, and Marcelo Luis Errecalde. "Early text classification: a Naïve solution." In Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/w16-0416.

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Reports on the topic "Analysis of Text Classification"

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Furey, John, Austin Davis, and Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41960.

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The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in nonquantitative text descriptions. We present initial text mining analyses of parsed text i
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Seo, Young-Woo, Joseph Giampapa, and Katia Sycara. Text Classification for Intelligent Portfolio Management. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada595830.

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Dasigi, V. R., R. C. Mann, and V. Protopopescu. Multi-sensor text classification experiments -- a comparison. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/638201.

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Han, Euihong, George Karypis, and Vipin Kumar. Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada439688.

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Dasigi, V. R., and R. C. Mann. Toward a multi-sensor-based approach to automatic text classification. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/130610.

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Dewdney, Nigel, Carol VanEss-Dykema, and Richard MacMillan. The Form is the Substance: Classification of Genres in Text. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada460898.

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Stevenson, Mark. Individual Profiling Using Text Analysis. Defense Technical Information Center, 2016. http://dx.doi.org/10.21236/ad1009417.

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Montiel Olea, César E., and Leonardo R. Corral. Text Analysis of Project Completion Reports. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003611.

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Project Completion Reports (PCRs) are the main instrument through which different multilateral organizations measure the success of a project once it closes. PCRs are important for development effectiveness as they serve to understand achievements, failures, and challenges within the project cycle they can feed back into the design and execution of new projects. The aim of this paper is to introduce text analysis tools for the exploration of PCR documents. We describe and apply different text analysis tools to explore the content of a sample of PCRs. We seek to illustrate a way in which PCRs c
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Pavlicheva, E. N., V. P. Meshalkin, and N. S. CHikunov. Algorithm for processing text data for an automatic classification problem using the word2vec method. OFERNIO, 2021. http://dx.doi.org/10.12731/ofernio.2021.24759.

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Bock, Geoffrey. Meta Tagging and Text Analysis from ClearForest. Patricia Seybold Group, 2002. http://dx.doi.org/10.1571/pr2-21-02cc.

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