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

IMDb sentiment analysis with GloVe embeddings

In Chapter 2, Understanding Sentiment in Natural Language with BiLSTMs, a BiLSTM model was built to predict the sentiment of IMDb movie reviews. That model learned embeddings of the words from scratch. This model had an accuracy of 83.55% on the test set, while the SOTA result was closer to 97.4%. If pre-trained embeddings are used, we expect an increase in model accuracy. Let's try this out and see the impact of transfer learning on this model. But first, let's understand the GloVe embedding model.

GloVe embeddings

In Chapter 1, Essentials of NLP, we discussed the Word2Vec algorithm, which is based on skip-grams with negative sampling. The GloVe model came out in 2014, a year after the Word2Vec paper came out. The GloVe and Word2Vec models are similar as the embeddings generated for a word are determined by the words that occur around it. However, these context words occur with different frequencies. Some of...