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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

Data

In order to build our search and retrieval system, we will use the same data for training and testing. We will use the Quora duplicate questions data for building our approach to searching and retrieving. The task of the Quora duplicate question pair is to determine whether two pairs of questions have the same meaning. The data contains a pair of questions and a ground truth label, marked by a human expert, mentioning whether the pairs of questions are duplicates. The ground truth data provided also mentions that these labels are subjective, meaning that not all human experts might agree on whether the pair of questions is similar. Hence, the data should be taken as informed, and not 100% accurate, due to the inherent subjectivity in the text data.

Data description

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