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)
Other Books You May Enjoy

Evaluating summaries

When people write summaries, they use inventive language. Human-written summaries often use words that are not present in the vocabulary of the text being summarized. When models generate abstractive summaries, they may also use words that are different from the words used in the ground truth summaries provided. There is no real way to do an effective semantic comparison of the ground truth summary and the generated summary. In summarization problems, a human evaluation step is often involved, which is where a qualitative check of the generated summaries is done. This method is both unscalable and expensive. There are approximations that uses n-gram overlaps and the longest common subsequence matches after stemming and lemmatization. The hope is that such pre-processing helps bring ground truth and generated summaries closer together for evaluation. The most common metric used for evaluating summaries is Recall-Oriented Understudy for Gisting Evaluation, also...