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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

Classification with Keras

For our experiments, we will be using the IMDB sentiment classification task. This is quite the small dataset - we are using it for the convenience of loading it and using it, as it is easily available via Keras. It is very important to understand here that for datasets of the size we are using, it is not the best idea to use a Deep Neural Network for classification - indeed, we might even get better results with a simple bag of words followed by a Support Vector Machine (SVM) doing the classification. The purpose of the following examples is to rather allow the user to understand how to construct a neural network using Keras, and how to make predictions using it. The fine tuning of the neural network and studying its hyperparameters is a different ball game altogether and is not the focus of this chapter. Another thing to remember when working with text...

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