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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ROUGE metric evaluation

A summary that's generated by a model should be readable, coherent, and factually correct. In addition, it should be grammatically correct. Human evaluation of summaries can be a mammoth task. If a person took 30 seconds to evaluate one summary in the Gigaword dataset, then it would take over 26 hours for one person to check the validation set. Since abstractive summaries are being generated, this human evaluation work will need to be done every time summaries are produced. The ROUGE metric tries to measure various aspects of an abstractive summary. It is a collection of four metrics:

  • ROUGE-N is the n-gram recall between a generated summary and the ground truth or reference summary. "N" at the end of the name specifies the length of the n-gram. It is common to report ROUGE-1 and ROUGE-2. The metric is calculated as the ratio of matching n-grams between the ground truth summary and the generated summary, divided by the total...