Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models.

Features:

Presents a comprehensive study of the interdisciplinary graphical approach to NLP
Covers recent computational intelligence techniques for graph-based neural network models
Discusses advances in random walk-based techniques, semantic webs, and lexical networks
Explores recent research into NLP for graph-based streaming data
Reviews advances in knowledge graph embedding and ontologies for NLP approaches

This book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.

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Yes, you can access Graph Learning and Network Science for Natural Language Processing by Muskan Garg, Amit Kumar Gupta, Rajesh Prasad, Muskan Garg,Amit Kumar Gupta,Rajesh Prasad in PDF and/or ePUB format, as well as other popular books in Economics & Computer Science General. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Year

Print ISBN

eBook ISBN

Edition

Topic

Economics

Subtopic

Computer Science General

Index

Economics

Cover
Half-Title
Series
Title
Copyright
Contents
Editors
Contributors
Preface
Chapter 1 Graph of Words Model for Natural Language Processing
Chapter 2 Application of NLP Using Graph Approaches
Chapter 3 Graph-based Extractive Approach for English and Hindi Text Summarization
Chapter 4 Graph Embeddings for Natural Language Processing
Chapter 5 Natural Language Processing with Graph and Machine Learning Algorithms-based Large-scale Text Document Summarization and Its Applications
Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing
Chapter 7 Ontology and Knowledge Graphs for Natural Language Processing
Chapter 8 Perfect Coloring by HB Color Matrix Algorithm Method
Chapter 9 Cross-lingual Word Sense Disambiguation Using Multilingual Co-occurrence Graphs
Chapter 10 Study of Current Learning Techniques for Natural Language Processing for Early Detection of Lung Cancer
Chapter 11 A Critical Analysis of Graph Topologies for Natural Language Processing and Their Applications
Chapter 12 Graph-based Text Document Extractive Summarization
Chapter 13 Applications of Graphical Natural Language Processing
Chapter 14 Analysis of Medical Images Using Machine Learning Techniques
Index

About this book

Frequently asked questions

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Table of contents