SUBMIT BOOK PROPOSAL
Online Store
benthambooks logo

Site Breadcrumb

A Handbook of Computational Linguistics: Artificial Intelligence in Natural Language Processing
View SampleDownload Sample

Editors: Youddha Beer Singh, Aditya Dev Mishra, Pushpa Singh, Dileep Kumar Yadav

Series Title: Federated Learning for Internet of Vehicles: IoV Image Processing, Vision and Intelligent Systems

A Handbook of Computational Linguistics: Artificial Intelligence in Natural Language Processing

Volume 2
eBook: US $79 Special Offer (PDF + Printed Copy): US $135
Printed Copy: US $95
Library License: US $316
ISBN: 978-981-5238-49-5 (Print)
ISBN: 978-981-5238-48-8 (Online)
Year of Publication: 2024
DOI: 10.2174/97898152384881240201
Rate This Book

Introduction

This handbook provides a comprehensive understanding of computational linguistics, focusing on the integration of deep learning in natural language processing (NLP). 18 edited chapters cover the state-of-the-art theoretical and experimental research on NLP, offering insights into advanced models and recent applications.

Highlights:

  • - Foundations of NLP: Provides an in-depth study of natural language processing, including basics, challenges, and applications..
  • - Advanced NLP Techniques: Explores recent advancements in text summarization, machine translation, and deep learning applications in NLP.
  • - Practical Applications: Demonstrates use cases on text identification from hazy images, speech-to-sign language translation, and word sense disambiguation using deep learning.
  • - Future Directions: Includes discussions on the future of NLP, including transfer learning, beyond syntax and semantics, and emerging challenges.

Key features:

  • - Comprehensive coverage of NLP and deep learning integration.
  • - Practical insights into real-world applications.
  • - Detailed exploration of recent research and advancements through 16 easy to read chapters
  • - References and notes on experimental methods used for advanced readers

Ideal for researchers, students, and professionals, this book offers a thorough understanding of computational linguistics by equipping readers with the knowledge to understand how computational techniques are applied to understand text, language and speech.

Audience:

Researchers, students, and professionals in computer science and related fields (AI, ML, NLP and computational linguistics).


eBook: US $79 Special Offer: US $135 Printed Copy: US $95 Library License: US $316

Preface

Natural Language processing is one of the fast-growing research areas that benefit the real world in various aspects. It gives the ability to machines to understand the text and audio in an efficient manner as human beings. NLP drives program code that supports virtual assistants, a voice-operated GPS system, text-to-speech transformation, and many more. NLP supports the creation of modern computers that understand human language with the help of deep learning and computational linguistics. Deep learning plays an important role in the processing of natural languages for various regional languages.

Natural Language Processing: A Handbook of Computational Linguistics covers chapters that focus on recent research in the form of reviews, surveys, technical articles, and state of art approaches. Through its numerous chapters, this edited book aims to include concepts in various areas such as recent developments and challenges in NLP, recent applications, learning techniques, text and sentence classification, speech technologies, machine translation, advances in information retrieval, and Indian language technologies. The objective of this book is to help researchers, academicians, and industry experts to give an idea/ direction/Research gaps for further extended research work.

KEY FEATURES
  • This book aims to provide state-of-the-art theoretical and experimental research on Natural Language Processing by using deep learning. The scope of this book is not only limited to academicians and researchers but also industry experts who work in the area of Natural Language Processing by using deep learning. The proposed book is certainly beneficial for both the academician and industry experts in terms of knowledge and further extended research work. The proposed book would be also useful as a reference guide for researchers, students, and engineers working in the area of natural language processing and deep learning.
  • With the aid of various linguistic, statistical, and machine-learning techniques, text analytics transforms unstructured text data into information that can be analyzed. Even though organizations may find sentiment analysis intimidating, particularly if they have a sizable customer base, an NLP tool will often comb through consumer interactions, such as social media comments or reviews, or even brand name mentions, to see what is being said.
  • When attempting to converse with someone who speaks a different language, language translation is of great assistance. Additionally, tools now identify the target language based on text input when translating from a different language to your own.
  • We take for-granted features on our smartphones like autocorrect, autocomplete, and predictive text because they are so frequent. In that, they anticipate what you will type and either complete the word you are typing or propose a related one, autocomplete and predictive text are comparable to search engines.

This book certainly motivates the reader to work in the field of NLP by using deep learning. This book may also be used as a reference book for graduates/postgraduate students studying computer science, information technology, and electronics and communication engineering.




Youddha Beer Singh
Galgotias College of Engineering and Technology
Greater Noida
India

Aditya Dev Mishra
Galgotias College of Engineering and Technology
Greater Noida
India

Pushpa Singh
GL Bajaj Institute of Technology & Management
Greater Noida
India

&

Dileep Kumar Yadav
Bennett University, Greater Noida
India