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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

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781800200937
Length 380 pages
Edition 1st Edition
Languages
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Authors (2):
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Tony Mullen Tony Mullen
Author Profile Icon Tony Mullen
Tony Mullen
Ashish Bansal Ashish Bansal
Author Profile Icon Ashish Bansal
Ashish Bansal
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Toc

Table of Contents (13) Chapters Close

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

Image captioning

Image captioning is all about describing the contents of an image in a sentence. Captions can help in content-based image retrieval and visual search. We already discussed how captions could improve the accessibility of websites by making it easier for screen readers to summarize the content of an image. A caption can be considered a summary of the image. Once we frame the problem as an image summarization problem, we can adapt the seq2seq model from the previous chapter to solve this problem. In text summarization, the input is a sequence of the long-form article, and the output is a short sequence summarizing the content. In image captioning, the output is similar in format to summarization. However, it may not be obvious how to structure an image that consists of pixels as a sequence of embeddings to be fed into the Encoder.

Secondly, the summarization architecture used Bi-directional Long Short-Term Memory networks (BiLSTMs), with the underlying principle...

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