Book Image

Advanced Natural Language Processing with TensorFlow 2

By : Ashish Bansal, Tony Mullen
Book Image

Advanced Natural Language Processing with TensorFlow 2

By: Ashish Bansal, Tony Mullen

Overview of this book

Recently, there have been tremendous advances in NLP, and we are now moving from research labs into practical applications. This book comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques. The book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It helps you apply the concepts of pre-processing text using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. You will build Named Entity Recognition (NER) from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs. The book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbots. You will learn how to apply transfer learning and fine-tuning using TensorFlow 2. Further, it covers practical techniques that can simplify the labelling of textual data. The book also has a working code that is adaptable to your use cases for each tech piece. By the end of the book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems.
Table of Contents (13 chapters)
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...