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 40 articles )

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  1. Chacoma, Andrés; Zanette, Damián H.: Word frequency-rank relationship in tagged texts (2021)
  2. Cozman, Fabio Gagliardi; Munhoz, Hugo Neri: Some thoughts on knowledge-enhanced machine learning (2021)
  3. Hannah Eyre, Alec B Chapman, Kelly S Peterson, Jianlin Shi, Patrick R Alba, Makoto M Jones, Tamara L Box, Scott L DuVall, Olga V Patterson: Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python (2021) arXiv
  4. Krasanakis, Emmanouil; Symeonidis, Andreas: Defining behaviorizeable relations to enable inference in semi-automatic program synthesis (2021)
  5. Niu, Yi-Shuai; You, Yu; Xu, Wenxu; Ding, Wentao; Hu, Junpeng; Yao, Songquan: A difference-of-convex programming approach with parallel branch-and-bound for sentence compression via a hybrid extractive model (2021)
  6. Ostovar, Ahmad; Bensch, Suna; Hellström, Thomas: Natural language guided object retrieval in images (2021)
  7. Sai Muralidhar Jayanthi, Kavya Nerella, Khyathi Raghavi Chandu, Alan W Black: CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing (2021) arXiv
  8. Škrlj, Blaž; Martinc, Matej; Lavrač, Nada; Pollak, Senja: autoBOT: evolving neuro-symbolic representations for explainable low resource text classification (2021)
  9. Liberti, Leo: Distance geometry and data science (2020)
  10. Lyndon White; Ayush Kaushal; Mike J Innes; Rohit Kumar: WordTokenizers.jl: Basic tools for tokenizing natural language in Julia (2020) not zbMATH
  11. Raeid Saqur, Ameet Deshpande: CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes (2020) arXiv
  12. Schnaubelt, Matthias; Fischer, Thomas G.; Krauss, Christopher: Separating the signal from the noise -- financial machine learning for Twitter (2020)
  13. François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
  14. Jibril Frej, Didier Schwab, Jean-Pierre Chevallet: WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset (2019) arXiv
  15. Gudivada, Venkat N.; Arbabifard, Kamyar: Open-source libraries, application frameworks, and workflow systems for NLP (2018)
  16. Kaya, Oguz; Uçar, Bora: Parallel Candecomp/Parafac decomposition of sparse tensors using dimension trees (2018)
  17. Lovaglio, Pietro Giorgio; Cesarini, Mirko; Mercorio, Fabio; Mezzanzanica, Mario: Skills in demand for ICT and statistical occupations: evidence from web-based job vacancies (2018)
  18. Zhang, Yazhou; Song, Dawei; Zhang, Peng; Wang, Panpan; Li, Jingfei; Li, Xiang; Wang, Benyou: A quantum-inspired multimodal sentiment analysis framework (2018)
  19. Alsinet, Teresa; Argelich, Josep; Béjar, Ramón; Fernández, Cèsar; Mateu, Carles; Planes, Jordi: Weighted argumentation for analysis of discussions in Twitter (2017)
  20. Christophe Van Gysel, Evangelos Kanoulas, Maarten de Rijke: Pyndri: a Python Interface to the Indri Search Engine (2017) arXiv

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