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Natural Language Processing with TensorFlow

You're reading from Natural Language Processing with TensorFlow Teach language to machines using Python's deep learning library

Product type Paperback

Published in May 2018

Publisher Packt

ISBN-13 9781788478311

Length 472 pages

Edition 1st Edition

Languages

Processing

Tools

Processing

Concepts

Deep Learning

Authors (2):

Thushan Ganegedara

Motaz Saad

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Table of Contents (14) Chapters

Preface

1. Introduction to Natural Language Processing FREE CHAPTER

2. Understanding TensorFlow

3. Word2vec – Learning Word Embeddings

4. Advanced Word2vec

5. Sentence Classification with Convolutional Neural Networks

6. Recurrent Neural Networks

7. Long Short-Term Memory Networks

8. Applications of LSTM – Generating Text

9. Applications of LSTM – Image Caption Generation

10. Sequence-to-Sequence Learning – Neural Machine Translation

11. Current Trends and the Future of Natural Language Processing

A. Mathematical Foundations and Advanced TensorFlow

Index

Evaluating the results quantitatively

There are many different techniques for evaluating the quality and the relevancy of the captions generated. We will briefly discuss several such metrics we can use to evaluate the captions. We will discuss four metrics: BLEU, ROGUE, METEOR, and CIDEr. All these measures share a key objective, to measure the adequacy (meaning of generated text) and fluency (grammatical correctness of text) in the generated text. To calculate all these measures, we will use a candidate sentence and a reference sentence, where a candidate sentence is the sentence/phrase predicted by our algorithm and the reference sentence is the true sentence/phrase we want to compare with.

BLEU

Bilingual Evaluation Understudy (BLEU) was proposed by Papineni and others in BLEU: A Method for Automatic Evaluation of Machine Translation, Proceedings of the 40^th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, July (2002): 311-318. It measures the n-gram...

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Authors (2)

Ganegedara

Ganegedara

Thushan is a seasoned ML practitioner with 4+ years of experience in the industry. Currently he is a senior machine learning engineer at Canva; an Australian startup that founded the online visual design software, Canva, serving millions of customers. His efforts are particularly concentrated in the search and recommendations group working on both visual and textual content. Prior to Canva, Thushan was a senior data scientist at QBE Insurance; an Australian Insurance company. Thushan was developing ML solutions for use-cases related to insurance claims. He also led efforts in developing a Speech2Text pipeline there. He obtained his PhD specializing in machine learning from the University of Sydney in 2018.

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Saad

Saad

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