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Hands-On Natural Language Processing with Python

You're reading from   Hands-On Natural Language Processing with Python A practical guide to applying deep learning architectures to your NLP applications

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781789139495
Length 312 pages
Edition 1st Edition
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Authors (5):
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Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
Chaitanya Joshi Chaitanya Joshi
Author Profile Icon Chaitanya Joshi
Chaitanya Joshi
Auguste Byiringiro Auguste Byiringiro
Author Profile Icon Auguste Byiringiro
Auguste Byiringiro
Rajesh Arumugam Rajesh Arumugam
Author Profile Icon Rajesh Arumugam
Rajesh Arumugam
Karthik Muthuswamy Karthik Muthuswamy
Author Profile Icon Karthik Muthuswamy
Karthik Muthuswamy
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Table of Contents (15) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Text Classification and POS Tagging Using NLTK 3. Deep Learning and TensorFlow 4. Semantic Embedding Using Shallow Models 5. Text Classification Using LSTM 6. Searching and DeDuplicating Using CNNs 7. Named Entity Recognition Using Character LSTM 8. Text Generation and Summarization Using GRUs 9. Question-Answering and Chatbots Using Memory Networks 10. Machine Translation Using the Attention-Based Model 11. Speech Recognition Using DeepSpeech 12. Text-to-Speech Using Tacotron 13. Deploying Trained Models 14. Other Books You May Enjoy

Text Classification Using LSTM

Text classification is the task of tagging natural language texts or unstructured text to one of the categories from a predefined set. Identifying positive-negative sentiments in product reviews, categorizing news articles, and segmenting customers based on their conversations about products in social media are some of the applications of text classification. One real-world example is automatic spam detection using machine learning (ML) in Gmail. The main goal of this chapter is to make the reader understand and get familiar with hands-on deep learning approaches for text classification.

In the previous chapters (Chapter 2, Text Classification and POS Tagging Using NLTK), we briefly touched upon text classification using classic ML approaches using a bag-of-words models with NLTK and sklearn. In this chapter, we will dive deeper into classifying...

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