Hands-on Question Answering Systems with BERT: Applications in Neural Networks and Natural Language Processing

Building a question-answering system with BERT means understanding more than the model itself. This book first lays out the NLP and neural-network concepts behind the technology, then follows those foundations toward practical question-answering applications. The result is a guided look at how BERT fits into the larger process of designing language systems.

Start with the foundations of language technology

The opening chapters introduce natural language processing, including tasks such as sentence segmentation, stemming and lemmatization, named entity recognition, and coreference resolution. From there, the authors explain neural networks used in NLP and introduce word embeddings—the representations that help computational systems work with language.

Understand what happens inside BERT

Rather than treating BERT as a black box, the book examines its key algorithms, including masked language modeling and next-sentence prediction. It also discusses BERT-based model variations and text-classification applications, giving readers a broader view of the techniques that inform practical use.

Move from model concepts to question answering

The central application section focuses on designing a BERT-based question-answering system. It also explores open-domain question answering and a DeepPavlov QA system. This progression helps connect the underlying NLP ideas with the components and decisions involved in building an applied system.

Explore related tasks and future directions

Beyond question answering, the book considers other BERT applications, including named entity recognition. Its closing discussion turns to future directions such as abstractive summarization and natural language generation.

Who may find it useful

AI and machine-learning developers, NLP practitioners, and readers building their understanding of BERT can use this book as a structured route from core concepts to question-answering applications. Its chapter sequence is especially helpful for readers who want the context behind the model alongside examples of where it can be applied.