Graph Learning and Network Science for Natural Language Processing
- Length: 256 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2022-12-28
- ISBN-10: 1032224568
- ISBN-13: 9781032224565
- Sales Rank: #12015256 (See Top 100 Books)
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.
Cover Half Title Series Page Title Page Copyright Page Table of Contents Editors Contributors Preface Chapter 1 Graph of Words Model for Natural Language Processing 1.1 Introduction 1.1.1 Lexical and Morphological Analysis 1.1.2 Syntactic Analysis 1.1.3 Semantic Analysis 1.1.4 Discourse Integration 1.1.5 Pragmatic Analysis 1.2 Machine Learning and Text Modelling 1.3 BoW Model 1.3.1 Introduction 1.3.1.1 Step 1: Collect the Data 1.3.1.2 Step 2: Vocabulary Design 1.3.1.3 Step 3: Document Vectors Creation 1.3.1.4 Scoring Words 1.3.2 Limitations of the BoW Model 1.4 Graph of Words (GoW) Model 1.4.1 Basic Terminology of Graphs 1.4.1.1 Real-world Graphs 1.4.1.2 Graphs in Linguistics 1.4.2 Semantic Similarity and Ambiguity 1.4.3 How to Build a GoW 1.4.3.1 Preliminary Concepts 1.4.4 Construction of a GoW 1.4.5 Use of GoW in Text Mining 1.4.6 GoW Mining 1.4.6.1 Graph Degeneracy 1.4.6.2 K-core Decomposition 1.4.6.3 K-truss 1.5 Discussion and Future Scope References Chapter 2 Application of NLP Using Graph Approaches 2.1 Introduction 2.1.1 What Is a Graph? 2.2 Graph Embeddings 2.3 Dynamic Graph of Words 2.4 Cross-lingual and Multilingual Graphical Approaches 2.5 Topological Analysis of Graphs 2.6 Adversarial Networks for Natural Language Processing 2.7 Heterogeneous Information Networks for Textual Information 2.8 Summary of Ontology and Knowledge Graphs 2.9 Topic Identification 2.10 Major Processes of NLP Using Graphical Approaches and Their Applications in the Real World 2.10.1 Summarization 2.10.1.1 News 2.10.1.2 Assignments and E-learning 2.10.1.3 Summarization of Financial or Legal Documents 2.10.2 Semi-supervised Passage Retrieval 2.10.3 Keyword Extraction 2.10.3.1 The Steps of the TextRank Algorithm 2.10.4 Information Extraction 2.10.5 Question Answering 2.10.6 Cross-language Information Retrieval 2.10.7 Term Weighting 2.10.8 Topic Segmentation 2.10.8.1 Graph-based Topic Segmentation 2.10.9 Machine Translation 2.10.9.1 Graph-based Machine Translation 2.10.10 Discourse Analysis 2.11 Conclusion and Future Scope of NLP 2.12 Datasets for NLP Applications References Chapter 3 Graph-based Extractive Approach for English and Hindi Text Summarization 3.1 Introduction 3.2 Text Summarization Approaches 3.2.1 Text Summarization Based on Number of Documents 3.2.2 Text Summarization Based on the Summary’s Purpose 3.2.3 Text Summarization Techniques 3.2.4 Text Summarization Based on Level of Language 3.2.5 Text Summarization Based on Output Style 3.2.6 Text Summarization Based on the Summary’s Characteristics 3.3 Literature Survey 3.4 Graph-based Algorithms 3.4.1 PageRank Algorithm 3.4.2 Text Rank Algorithm 3.5 TF-IDF Algorithm 3.6 Methodology 3.7 Experimental Results 3.7.1 English Original Text 3.7.2 Summary Produced Using the Text Rank Algorithm 3.7.3 Summary Produced Using the TF-IDF Algorithm 3.7.4 Hindi Original Text 3.7.5 Summary Produced Using the Text Rank Algorithm 3.7.6 Summary Produced Using the TF-IDF Algorithm 3.8 Conclusions and Future Directions References Chapter 4 Graph Embeddings for Natural Language Processing 4.1 Introduction 4.1.1 Natural Language and Natural Language Processing 4.1.2 Processing: A Module in Machine Learning 4.2 Computational Techniques 4.2.1 How NLP Works 4.2.2 Graph Embeddings 4.2.2.1 Graph 4.2.2.2 Embedding 4.2.2.3 Graph Embeddings 4.2.2.4 Word Embeddings: Classic Example 4.3 The Singular Value Decomposition for “Graph Embeddings” 4.4 Predictive Methods 4.4.1 Word2vec 4.4.1.1 CBOW: Continuous Bag of Words [5] 4.4.1.2 Skip-gram Model 4.5 More Embedding Techniques 4.6 Conclusion 4.7 Case Study: Neo4j Lab Implementations References Chapter 5 Natural Language Processing with Graph and Machine Learning Algorithms-based Large-scale Text Document Summarization and Its Applications 5.1 Introduction 5.1.1 Types of Text Summary 5.2 Text Summarization and Machine Learning 5.2.1 What Is Graph ML? 5.2.2 Simple Algorithmic Approach 5.3 Literature Survey 5.3.1 Gap Analysis 5.4 Problem Statement 5.5 System Architecture 5.6 Graph-based Solutions 5.7 Conclusion References Chapter 6 Ontology and Knowledge Graphs for Semantic Analysis in Natural Language Processing 6.1 Introduction 6.2 Background 6.2.1 Semantics in NLP 6.2.1.1 Meaning Representation 6.2.2 Semantic Analysis in Natural Language Processing 6.3 Semantic Technologies 6.3.1 Ontology Essentials 6.3.2 Formalization of an Ontology 6.3.3 Description Logics 6.3.4 Ontological Languages 6.3.5 Knowledge Graphs 6.3.5.1 Ontology and Knowledge Graphs 6.3.5.2 Property Graphs 6.3.5.3 Comparison of KGs, PGs, and Ontologies 6.4 The Role of Ontology and Knowledge Graphs in Semantic Analysis 6.4.1 The Role of Semantic Technology in Knowledge-based NLP Applications 6.4.2 The Role of Semantic Technology Machine Learning NLP Applications 6.4.3 The Role of Natural Language Processing in Ontology Generation 6.5 Review of Developments in Ontological Semantic Analysis 6.6 Summary References Chapter 7 Ontology and Knowledge Graphs for Natural Language Processing 7.1 Introduction 7.1.1 Ontology 7.1.1.1 The Core Ideas of Ontology 7.2 Natural Language Processing 7.2.1 Dealing with Huge Amounts of Unstructured Data 7.2.2 Structuring Data to Support Intelligent Systems 7.2.3 Challenges with NLP 7.3 Ontology and Knowledge Graphs for NLP 7.3.1 Ontology 7.3.2 Knowledge Graphs 7.4 Ontological Languages 7.5 Conclusion References Chapter 8 Perfect Coloring by HB Color Matrix Algorithm Method 8.1 Introduction 8.2 Preliminaries 8.2.1 HB Color Matrix 8.2.2 Perfect Coloring 8.3 Results 8.3.1 Perfect HB Color Matrix 8.3.1.1 Example of a PHBCM 8.3.1.2 Properties of a PHBCM 8.3.2 Algorithm of Perfect Coloring by HB Color Matrix Method 8.4 Illustration of the Perfect HB Color Matrix Method 8.5 Python Program for Graph Coloring by PHBCM 8.6 The Perfect Chromatic Numbers for Some Standard Graphs Using the PHBCM Algorithm Method 8.7 Conclusion References Chapter 9 Cross-lingual Word Sense Disambiguation Using Multilingual Co-occurrence Graphs 9.1 Introduction 9.2 Evaluation of WSD 9.2.1 Types of WSD 9.2.2 Difficulties in Word Sense Disambiguation 9.2.2.1 Differences between Dictionaries 9.2.2.2 Part of Speech Tagging 9.2.2.3 Inter-judge Variance 9.2.2.4 Pragmatics 9.2.2.5 Sense Inventories and Task-dependent Algorithms 9.2.2.6 Sense Discreteness 9.3 Approaches to Word Sense Disambiguation 9.3.1 Dictionary and Knowledge-based Methods 9.3.2 Supervised Methods 9.3.3 Semi-Supervised Methods 9.3.4 Unsupervised Methods 9.4 Graph-based Cross-Lingual Word Sense Disambiguation 9.4.1 MultiMirror Model 9.4.2 UHD Model 9.4.3 Co-occurrence Graphs for WSD in the Biomedical Domain 9.4.4 WSD Based on Word Similarity Calculation Using Weighted Voronoi Regions from a Knowledge Graph 9.4.5 Graph Convolutional Networks for WSD 9.4.6 The Context Expansion Approach in Graph WSD 9.4.7 WSD Using WordNet Knowledge Graphs 9.5 Applications of WSD 9.6 Conclusions and Future Scope References Chapter 10 Study of Current Learning Techniques for Natural Language Processing for Early Detection of Lung Cancer 10.1 Introduction 10.2 Rationale and Significance of the Study 10.3 Motivation 10.4 Learning Techniques 10.4.1 Data Annotation 10.4.2 Word Embedding 10.4.3 NER Process 10.4.4 Relation Classification Process 10.4.5 Prediction Performance Step 10.5 Related Work 10.6 Discussion 10.7 Conclusion References Chapter 11 A Critical Analysis of Graph Topologies for Natural Language Processing and Their Applications 11.1 Introduction 11.1.1 Natural Language Processing 11.1.2 Tools and Libraries for NLP 11.1.3 Graph of Words and Graph -based Natural Language Generation 11.4 Graph Embedding in NLP 11.4.1 Node Embeddings 11.4.1.1 Matrix Factorization Methods 11.4.1.2 Graph Neural Network Methods 11.4.1.3 Random Walk (RW) Methods 11.4.1.4 Applications of Node Embedding 11.4.2 Relation Embedding 11.4.2.1 Knowledge-based Relation Embedding 11.4.2.2 Unsupervised Relation Embedding 11.4.2.3 Applications of Relation Embedding 11.5 Graph Topologies for NLP Applications 11.5.1 Critical Analysis of Graph Architectures for NLP Applications 11.5.1.1 Text Formation, Conversation, and Generation 11.5.1.2 Language Rules and Classification 11.5.1.3 Context 11.5.1.4 Machine Translation 11.5.1.5 Knowledge Mining and Demonstration 11.6 Conclusion and Future Work References Chapter 12 Graph-based Text Document Extractive Summarization 12.1 Introduction 12.2 Extractive Summarization 12.2.1 Commonly used Features in Extractive Summarization Method 12.2.2 Summary Length 12.3 Graph-based Methods for Extractive Summarization 12.3.1 Graph-based Ranking Algorithm 12.3.2 Weighted/unweighted Simple Graph 12.3.3 Heterogeneous Graph Model 12.3.4 Correlation Graph Model 12.3.5 Semantic Graph Model 12.3.6 Hypergraph Model 12.3.6.1 Hypergraph Construction 12.3.7 Semigraph Model 12.4 Conclusion References Chapter 13 Applications of Graphical Natural Language Processing 13.1 Graph Theory in Natural Language Processing 13.2 Text Summarization 13.3 Keyword Extraction 13.4 Graph -oriented Topic Analysis 13.5 Topic Segmentation 13.6 Discourse Relationships 13.7 Machine Translation 13.8 Multilingual Retrieval of Information Based on Graphs 13.9 Information Retrieval Using Graphs 13.10 Graph -based Question Answering References Chapter 14 Analysis of Medical Images Using Machine Learning Techniques 14.1 Introduction 14.1.1 Overview 14.1.2 Motivation 14.1.3 Objective 14.1.3.1 Study of Different Segmentation Techniques 14.1.3.2 Selection for Appropriate Features 14.1.3.3 Performance and Classification of the Proposed Model 14.1.4 Expected Outcomes 14.2 Literature Survey 14.3 The Problem Domain and Proposed Solution 14.3.1 Domain Description 14.3.2 Problem domain 14.3.3 Solution Domain 14.3.4 Algorithms Used in the Study 14.3.4.1 Otsu’s Method 14.3.4.2 Wavelet Transformation 14.3.4.3 Principal Component Analysis 14.3.4.4 Gray Level Co-occurrence Matrix 14.3.4.5 Support Vector Machine 14.3.5 Our Proposed Algorithm 14.4 Implementation 14.4.1 Tools and Techniques 14.4.2 MATLAB 14.4.3 Methodology 14.5 Result Analysis 14.5.1 Means 14.5.2 Standard Deviation 14.5.3 Entropy 14.5.4 RMS 14.5.5 Variance 14.5.6 Smoothness 14.5.7 Kurtosis 14.5.8 Skewness 14.5.9 IDM 14.5.10 Contrast 14.5.11 Correlation 14.5.12 Energy 14.5.13 Homogeneity 14.5.14 Kernel Functions 14.6 Conclusion and Future Work 14.6.1 Conclusion 14.6.2 Future Work References Index
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