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Natural Language Processing and Computational Linguistics

You're reading from  Natural Language Processing and Computational Linguistics

Product type Book
Published in Jun 2018
Publisher Packt
ISBN-13 9781788838535
Pages 306 pages
Edition 1st Edition
Languages
Author (1):
Bhargav Srinivasa-Desikan Bhargav Srinivasa-Desikan
Profile icon Bhargav Srinivasa-Desikan

Table of Contents (22) Chapters

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. What is Text Analysis? 2. Python Tips for Text Analysis 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 1. Other Books You May Enjoy Index

References

[1] Latent Semantic Analysis:
https://en.wikipedia.org/wiki/Latent_semantic_analysis#Latent_semantic_indexing

[2] Gensim:
https://radimrehurek.com/gensim/

[3] Latent Dirichlet Allocation:
http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf

[4] Introduction to LDA:
http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/

[5] Explanation of LDA:
https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation

[6] Probabilistic Topic Models:
http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf

[7] Jupyter Notebook:
https://github.com/bhargavvader/personal/blob/master/notebooks/text_analysis_tutorial/topic_modelling.ipynb

[8] An Empirical Evaluation of Models of Text Document Similarity:
http://www.socsci.uci.edu/~mdlee/lee_pincombe_welsh_document.PDF

[9] Singular-Value Decomposition:
https://en.wikipedia.org/wiki/Singular...

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