Navin Sabharwal - Question Answering Systems with BERT: Applications in Neural Networks and Natural Language Processing

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-6663-2 . For more detailed information, please visit http://www.apress.com/source-code .

Question answering systems have revolutionized information retrieval. Technologies like Bidirectional Encoder Representations from Transformers (BERT) have made it possible for documents to be analyzed by machine learning systems and retrieve contextual information through the question-and-answer mechanism without the need for extensive training. Evolution of deep learning has had a great impact on the design of question answering systems and has enabled these systems to ingest enormous amounts of data and build billions of connections to understand human language better.

This book focuses on a recent breakthrough in the natural language processing (NLP) domain, BERT which has achieved benchmarks on state-of-art NLP tasks such as question answering system, entity recognition systems, and so on.

BERT implements innovative ways to generate the embedding of textual sentences. This book provides guidance on design and implementation of various types of question answering systems along with NLP tasks such as document summarization, entity recognition, and sentiment analysis. This could help data scientists and developers to design and implement their own NLP-based systems using BERT.

Lets start our journey in the exciting and quickly evolving domain of NLP.

I would like to thank my family, Shweta and Soumil, for always being by my side, for sacrificing their time for my intellectual and spiritual pursuits, and for taking care of everything while I was immersed in authoring this book. This and other accomplishments in my life wouldnt have been possible without their love and support. I also thank my mom and my sister for their love and support, as always; without their blessings, nothing is possible.

To my coauthor, Amit, I thank you for the hard work and quick turnarounds to deliver this book. It was an enriching experience, and I look forward to working with you again soon.

Thanks go to all of my team members, who have been a source of inspiration with their hard work, their always engaging technical conversations, and their technical depth. Your constantly flowing ideas are a source of encouragement and excitement every single day. Piyush Pandey, Sarvesh Pandey, Amit Agrawal, Vasand Kumar, Punith Krishnamurthy, Sandeep Sharma, Amit Dwivedi, Gauarv Bhardwaj, Nitin Narotra, Divjot, and Vivek, thank you for being there and making technology fun.

To all my other coauthors, colleagues, managers, mentors, and guides, in this world of 7 billion, it was coincidence that brought us together, but it has been an enriching experience to be associated with you and learn from you. All ideas and paths are an assimilation of conversations that I have had and experiences I have shared. Thank you.

Navin Sabharwal

I thank my parents, brothers, and wife Riya for always being an inspiration for me.

Thanks go to my coauthor, Navin, for his guidance and feedback.

I am also grateful to my colleagues Rishabh Upadhyay and Yogita Kanwar for their technical suggestions.

Amit Agrawal

Thank you goddess Saraswati, for guiding me to the path of knowledge and spirituality: , ,

Asato Ma Sad Gamaya, Tamaso Ma Jyotir Gamaya,

Mrityor Ma Amritam Gamaya

Lead us from ignorance to truth, lead us from

darkness to light, lead us from illusion to reality