Description

Book Synopsis

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

    Table of Contents

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

Graph Learning and Network Science for Natural

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    £105.00

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    Order before 4pm tomorrow for delivery by Tue 9 Jun 2026.

    A Hardback by Muskan Garg, Amit Kumar Gupta, Rajesh Prasad

    1 in stock


      View other formats and editions of Graph Learning and Network Science for Natural by Muskan Garg

      Publisher: Taylor & Francis Ltd
      Publication Date: 12/28/2022 12:00:00 AM
      ISBN13: 9781032224565, 978-1032224565
      ISBN10: 1032224568
      Also in:
      Computing Information retrieval

      Description

      Book Synopsis

      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

        Table of Contents

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

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