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)
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Overview of text summarization

The core idea in summarization is to condense long-form text or articles into a short representation. The shorter representation should contain the main idea of crucial information from the longer form. A single document can be summarized. This document could be long or may contain just a couple of sentences. An example of a short document summarization is generating a headline from the first few sentences of an article. This is called sentence compression. When multiple documents are being summarized, they are usually related. They could be the financial reports of a company or news reports about an event. The generated summary could itself be long or short. A shorter summary would be desirable when generating a headline. A lengthier summary would be something like an abstract and could have multiple sentences.

There are two main approaches when summarizing text:

  • Extractive summarization: Phrases or sentences from the articles are selected...