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Natural Language Processing and Computational Linguistics

You're reading from   Natural Language Processing and Computational Linguistics A practical guide to text analysis with Python, Gensim, spaCy, and Keras

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788838535
Length 306 pages
Edition 1st Edition
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Author (1):
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Bhargav Srinivasa-Desikan Bhargav Srinivasa-Desikan
Author Profile Icon Bhargav Srinivasa-Desikan
Bhargav Srinivasa-Desikan
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Table of Contents (17) Chapters Close

Preface 1. What is Text Analysis? 2. Python Tips for Text Analysis FREE CHAPTER 3. spaCy's Language Models 4. Gensim – Vectorizing Text and Transformations and n-grams 5. POS-Tagging and Its Applications 6. NER-Tagging and Its Applications 7. Dependency Parsing 8. Topic Models 9. Advanced Topic Modeling 10. Clustering and Classifying Text 11. Similarity Queries and Summarization 12. Word2Vec, Doc2Vec, and Gensim 13. Deep Learning for Text 14. Keras and spaCy for Deep Learning 15. Sentiment Analysis and ChatBots 16. Other Books You May Enjoy

References

[1] Efficient Estimation of Word Representations in Vector Space [Mikolov et al. 2013]:
https://arxiv.org/pdf/1301.3781.pdf

[2] Distributed Representations of Words and Phrases and their Compositionality [Mikolov et al. 2013]:
https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf

[3] Linguistic Regularities in Continuous Space Word Representations [Mikolov et al. 2013]:
http://www.aclweb.org/anthology/N13-1090

[4] Word2Vec Tutorial - The Skip-Gram Model:
http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
[5] Amazing power of word vectors:
https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/

[6] Word2Vec resources:
http://mccormickml.com/2016/04/27/word2vec-resources/

[7] Original C Word2Vec code:
https://code.google.com/archive/p/word2vec/

[8] Deep Learning with...

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