Hands-on Question Answering Systems with BERT

Applications in Neural Networks and Natural Language Processing
1. Auflage 2021
ISBN: 978-1-4842-6663-2
Verlag: Apress

Buch, Englisch, 184 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 312 g

Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning.

The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you’ll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you’ll cover word embedding and their types along with the basics of BERT.

After this solid foundation, you’ll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You’ll see different BERT variations followed by a hands-on example of a question answering system.

Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT.

What You Will Learn

- Examine the fundamentals of word embeddings
- Apply neural networks and BERT for various NLP tasks
Develop a question-answering system from scratch
- Train question-answering systems for your own data

Who This Book Is For

AI and machine learning developers and natural language processing developers.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Agrawal, Amit

Sabharwal, Navin

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Angewandte Informatik

Inhaltsverzeichnis

Chapter 1: Introduction to Natural Language Processing.- Chapter 2: Introduction to Word Embeddings.- Chapter 3: BERT Algorithms Explained.- Chapter 4: BERT Model Applications - Question Answering System.- Chapter 5: BERT Model Applications - Other tasks.- Chapter 6: Future of BERT models.

Über Autor(innen)

Navin is the chief architect for HCL DryICE Autonomics. He is an innovator, thought leader, author, and consultant in the areas of AI, machine learning, cloud computing, big data analytics, and software product development. He is responsible for IP development and service delivery in the areas of AI and machine learning, automation, AIOPS, public cloud GCP, AWS, and Microsoft Azure. Navin has authored 15+ books in the areas of cloud computing, cognitive virtual agents, IBM Watson, GCP, containers, and microservices.

Amit Agrawal is a senior data scientist and researcher delivering solutions in the fields of AI and machine learning. He is responsible for designing end-to-end solutions and architecture for enterprise products. He has also authored and reviewed books in the area of cognitive virtual assistants.