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Advanced Natural Language Processing with TensorFlow 2

You're reading from Advanced Natural Language Processing with TensorFlow 2 Build effective real-world NLP applications using NER, RNNs, seq2seq models, Transformers, and more

Product type Paperback

Published in Feb 2021

Publisher Packt

ISBN-13 9781800200937

Length 380 pages

Edition 1st Edition

Languages

Processing

Tools

Processing

Concepts

Mobile Application Development

Authors (2):

Tony Mullen

Ashish Bansal

View More author details

Table of Contents (13) Chapters

Preface

1. Essentials of NLP

2. Understanding Sentiment in Natural Language with BiLSTMs FREE CHAPTER

3. Named Entity Recognition (NER) with BiLSTMs, CRFs, and Viterbi Decoding

4. Transfer Learning with BERT

5. Generating Text with RNNs and GPT-2

6. Text Summarization with Seq2seq Attention and Transformer Networks

7. Multi-Modal Networks and Image Captioning with ResNets and Transformer Networks

8. Weakly Supervised Learning for Classification with Snorkel

9. Building Conversational AI Applications with Deep Learning

10. Installation and Setup Instructions for Code

11. Other Books You May Enjoy

12. Index

ROUGE metric evaluation

A summary that's generated by a model should be readable, coherent, and factually correct. In addition, it should be grammatically correct. Human evaluation of summaries can be a mammoth task. If a person took 30 seconds to evaluate one summary in the Gigaword dataset, then it would take over 26 hours for one person to check the validation set. Since abstractive summaries are being generated, this human evaluation work will need to be done every time summaries are produced. The ROUGE metric tries to measure various aspects of an abstractive summary. It is a collection of four metrics:

ROUGE-N is the n-gram recall between a generated summary and the ground truth or reference summary. "N" at the end of the name specifies the length of the n-gram. It is common to report ROUGE-1 and ROUGE-2. The metric is calculated as the ratio of matching n-grams between the ground truth summary and the generated summary, divided by the total...

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Authors (2)

Mullen

Mullen

See other products by Mullen

Ashish Bansal

Ashish Bansal

Ashish is an AI/ML leader, a well-known speaker, and an astute technologist with over 20 years of experience in the field. He has a Bachelor's in technology from IIT BHU, and an MBA in marketing from Kellogg School of Management. He is currently the Director of Recommendations at Twitch where he works on building scalable recommendation systems across a variety of product surfaces, connecting content to people. He has worked on recommendation systems at multiple organizations, most notably Twitter where he led Trends and Events recommendations and at Capital One where he worked on B2B and B2C products. Ashish was also a co-founder of GALE Partners, a full-service digital agency in Toronto, and spent over 9 years at SapientNitro, a leading digital agency.

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