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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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Concepts
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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
Bhargav Srinivasa-Desikan is a research engineer working for INRIA in Lille, France. He is a part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation, and data visualization. He is a regular contributor to the Python open source community, and completed Google Summer of Code in 2016 with Gensim where he implemented Dynamic Topic Models. He is a regular speaker at PyCons and PyDatas across Europe and Asia, and conducts tutorials on text analysis using Python.
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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] Vectors:
https://en.wikipedia.org/wiki/Euclidean_vector

[2] Scalability of Semantic Analysis in Natural Language Processing:
https://radimrehurek.com/phd_rehurek.pdf

[3] Latent Dirichlet allocation:
https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation

[4] Latent semantic indexing:
https://en.wikipedia.org/wiki/Latent_semantic_analysis#Latent_semantic_indexing

[5] Random Projection:
https://en.wikipedia.org/wiki/Random_projection

[6] Stanford TMT:
https://nlp.stanford.edu/software/tmt/tmt-0.4/

[7] Gensim notebooks:
https://github.com/RaRe-Technologies/gensim/tree/develop/docs/notebooks

[8] Jupyter Notebooks:
http://jupyter-notebook.readthedocs.io/en/stable/notebook.html

[9] Vector Space Models:
https://en.wikipedia.org/wiki/Vector_space_model

[10] Bayesian Probability:
https://en.wikipedia.org/wiki/Bayesian_probability

[11] TF-IDF:
https://en.wikipedia...

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Authors (1)

Profile icon Bhargav Srinivasa-Desikan
Bhargav Srinivasa-Desikan
Github icon
Bhargav Srinivasa-Desikan is a research engineer working for INRIA in Lille, France. He is a part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation, and data visualization. He is a regular contributor to the Python open source community, and completed Google Summer of Code in 2016 with Gensim where he implemented Dynamic Topic Models. He is a regular speaker at PyCons and PyDatas across Europe and Asia, and conducts tutorials on text analysis using Python.
Read more
See other products by Bhargav Srinivasa-Desikan

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