Neural Networks for Natural Language Processing

Neural Networks for Natural Language Processing

Noted as an IGI Global Core Reference Title in Computer Science & IT for 2019.

Sumathi S. (St. Joseph's College of Engineering, India) and Janani M. (St. Joseph's College of Engineering, India)
Release Date: November, 2019|Copyright: © 2020 |Pages: 227|DOI: 10.4018/978-1-7998-1159-6
ISBN13: 9781799811596|ISBN10: 179981159X|EISBN13: 9781799811619|ISBN13 Softcover: 9781799811602
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Description

Information in today’s advancing world is rapidly expanding and becoming widely available. This eruption of data has made handling it a daunting and time-consuming task. Natural language processing (NLP) is a method that applies linguistics and algorithms to large amounts of this data to make it more valuable. NLP improves the interaction between humans and computers, yet there remains a lack of research that focuses on the practical implementations of this trending approach.

Neural Networks for Natural Language Processing is a collection of innovative research on the methods and applications of linguistic information processing and its computational properties. This publication will support readers with performing sentence classification and language generation using neural networks, apply deep learning models to solve machine translation and conversation problems, and apply deep structured semantic models on information retrieval and natural language applications. While highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and development professionals, IT specialists, industrialists, technology developers, data analysts, data scientists, academics, researchers, and students seeking current research on the fundamental concepts and techniques of natural language processing.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Deep Learning
  • Information Retrieval
  • Knowledge Graph Generation
  • Machine Learning
  • Ontology Creation
  • Query Entity Recognition
  • Structured Data
  • Text Categorization
  • Text Classification
  • Text Summarization

Table of Contents and List of Contributors

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