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
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12
Index

Summary

Transfer learning has made a lot of progress possible in the world of NLP, where data is readily available, but labeled data is a challenge. We covered different types of transfer learning first. Then, we took pre-trained GloVe embeddings and applied them to the IMDb sentiment analysis problem, seeing comparable accuracy with a much smaller model that takes much less time to train.

Next, we learned about seminal moments in the evolution of NLP models, starting from encoder-decoder architectures, attention, and Transformer models, before understanding the BERT model. Using the Hugging Face library, we used a pre-trained BERT model and a custom model built on top of BERT for the purpose of sentiment classification of IMDb reviews.

BERT only uses the encoder part of the Transformer model. The decoder side of the stack is used in text generation. The next two chapters will focus on completing the understanding of the Transformer model. The next chapter will use the decoder...