Graph Learning and Network Science for Natural Language Processing
eBook - ePub

Graph Learning and Network Science for Natural Language Processing

  1. 256 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Graph Learning and Network Science for Natural Language Processing

Book details
Table of contents
Citations

About This Book

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.

Frequently asked questions

Simply head over to the account section in settings and click on “Cancel Subscription” - it’s as simple as that. After you cancel, your membership will stay active for the remainder of the time you’ve paid for. Learn more here.
At the moment all of our mobile-responsive ePub books are available to download via the app. Most of our PDFs are also available to download and we're working on making the final remaining ones downloadable now. Learn more here.
Both plans give you full access to the library and all of Perlego’s features. The only differences are the price and subscription period: With the annual plan you’ll save around 30% compared to 12 months on the monthly plan.
We are an online textbook subscription service, where you can get access to an entire online library for less than the price of a single book per month. With over 1 million books across 1000+ topics, we’ve got you covered! Learn more here.
Look out for the read-aloud symbol on your next book to see if you can listen to it. The read-aloud tool reads text aloud for you, highlighting the text as it is being read. You can pause it, speed it up and slow it down. Learn more here.
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 & Statistics for Business & Economics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
CRC Press
Year
2022
ISBN
9781000789508
Edition
1
Topic
Economics
Subtopic
Statistics for Business & Economics
Index
Economics

Table of contents

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