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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 2. Text Classification and POS Tagging Using NLTK FREE CHAPTER 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 for text classification

Before diving into the machine learning (ML) problems in text classification, we will take a look at the different open datasets that are available on the internet. Many of the classification tasks may require large labeled text data. This data can be broadly grouped into those with binary classes, multi-classes, and multi-labels. The following are some of the popular datasets used for benchmarking in both research and some competitions, such as Kaggle:

...
Dataset name
Class type
Source

1

IMDb movie Dataset

Binary classes

http://ai.stanford.edu/~amaas/data/sentiment/

2

Twitter Sentiment Analysis Dataset

Binary classes

http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/

3

YouTube Spam Collection Dataset

Binary classes

https://archive.ics.uci.edu/ml/datasets/YouTube+Spam+Collection

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