NLTK

This book offers an introduction to Natural Language Processing (NLP). Natural language means a language that is used for everyday communication by humans. The reader will learn how to write Python programs that work with large collections of unstructured text. A comprehensive range of linguistic data structures is presented as well as algorithms for analyzing the content and structure of written communication. NLP is experiencing rapid growth, and technologies based on NLP are becoming increasingly widespread, for example phones support predictive text and even handwriting recognition. The book contains hundreds of working examples and graded exercises, based on the Python programming language and the Natural Language Toolkit (NLTK), and it gives the reader a working knowledge of NLP


References in zbMATH (referenced in 30 articles )

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  1. Liberti, Leo: Distance geometry and data science (2020)
  2. Lyndon White; Ayush Kaushal; Mike J Innes; Rohit Kumar: WordTokenizers.jl: Basic tools for tokenizing natural language in Julia (2020) not zbMATH
  3. Schnaubelt, Matthias; Fischer, Thomas G.; Krauss, Christopher: Separating the signal from the noise -- financial machine learning for Twitter (2020)
  4. François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
  5. Jibril Frej, Didier Schwab, Jean-Pierre Chevallet: WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset (2019) arXiv
  6. Gudivada, Venkat N.; Arbabifard, Kamyar: Open-source libraries, application frameworks, and workflow systems for NLP (2018)
  7. Kaya, Oguz; Uçar, Bora: Parallel Candecomp/Parafac decomposition of sparse tensors using dimension trees (2018)
  8. Zhang, Yazhou; Song, Dawei; Zhang, Peng; Wang, Panpan; Li, Jingfei; Li, Xiang; Wang, Benyou: A quantum-inspired multimodal sentiment analysis framework (2018)
  9. Alsinet, Teresa; Argelich, Josep; Béjar, Ramón; Fernández, Cèsar; Mateu, Carles; Planes, Jordi: Weighted argumentation for analysis of discussions in Twitter (2017)
  10. Christophe Van Gysel, Evangelos Kanoulas, Maarten de Rijke: Pyndri: a Python Interface to the Indri Search Engine (2017) arXiv
  11. Dupuy, Christophe; Bach, Francis: Online but accurate inference for latent variable models with local Gibbs sampling (2017)
  12. Chandar, Sarath; Khapra, Mitesh M.; Larochelle, Hugo; Ravindran, Balaraman: Correlational neural networks (2016)
  13. Dhillon, Paramveer S.; Foster, Dean P.; Ungar, Lyle H.: Eigenwords: spectral word embeddings (2015)
  14. Li, Chun-Liang; Su, Yu-Chuan; Lin, Ting-Wei; Tsai, Cheng-Hao; Chang, Wei-Cheng; Huang, Kuan-Hao; Kuo, Tzu-Ming; Lin, Shan-Wei; Lin, Young-San; Lu, Yu-Chen; Yang, Chun-Pai; Chang, Cheng-Xia; Chin, Wei-Sheng; Juan, Yu-Chin; Tung, Hsiao-Yu; Wang, Jui-Pin; Wei, Cheng-Kuang; Wu, Felix; Yin, Tu-Chun; Yu, Tong; Zhuang, Yong; Lin, Shou-De; Lin, Hsuan-Tien; Lin, Chih-Jen: Combination of feature engineering and ranking models for paper-author identification in KDD cup 2013 (2015) ioport
  15. Mashechkin, I.; Petrovskiy, M.; Popov, D.; Tsarev, D.: Applying text mining methods for data loss prevention (2015) ioport
  16. Wu, Chung-Hsien; Liu, Chao-Hong; Su, Po-Hsun: Sentence extraction with topic modeling for question-answer pair generation (2015) ioport
  17. Drury, Brett; Cardoso, Paula; Valverde-Rebaza, Jorge; Valejo, Alan; Pereira, Fabio; Andrade Lopes, Alneu: An open source tool for crowd-sourcing the manual annotation of texts (2014) ioport
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  19. Hu, Yuening; Boyd-Graber, Jordan; Satinoff, Brianna; Smith, Alison: Interactive topic modeling (2014) ioport
  20. Silva, Ana Paula; Silva, Arlindo; Rodrigues, Irene: An approach to the POS tagging problem using genetic algorithms (2014) ioport

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