Hybrid Intelligent Systems for Information Retrieval

✍ Scribed by Anuradha D. Thakare, Shilpa Laddha, Ambika Pawar

• Publisher: CRC Press/Chapman & Hall
• Year: 2022
• Tongue: English
• Leaves: 253
• Series: Chapman & Hall/CRC Computational Intelligence and Its Applications
• Category: Library

✦ Synopsis

Hybrid Intelligent Systems for Information Retrieval covers three areas along with the introduction to Intelligent IR, i.e., Optimal Information Retrieval Using Evolutionary Approaches, Semantic Search for Web Information Retrieval, and Natural Language Processing for Information Retrieval.

• Talks about the design, implementation, and performance issues of the hybrid intelligent information retrieval system in one book

• Gives a clear insight into challenges and issues in designing a hybrid information retrieval system

• Includes case studies on structured and unstructured data for hybrid intelligent information retrieval

• Provides research directions for the design and development of intelligent search engines

This book is aimed primarily at graduates and researchers in the information retrieval domain.

✦ Table of Contents

Cover
Half Title
Series
Title
Copyright
Contents
Preface
Acknowledgments
Author Biographies
Chapter 1 Introduction
1.1 Information Retrieval Models
1.2 Introduction To Optimal Information Retrieval
1.3 Introduction To Semantic Web Retrieval
1.4 Introduction To Natural Language Processing For Information Retrieval
Chapter 2 Matching Functions
2.1 Classical Matching Functions
2.2 Hybrid Virtual Center Matching Function (Vcf) For Genetic Algorithm-Based Retrieval
2.3 Vcf-Based Information Retrieval
2.4 Hybrid Unification Scheme For Matching
2.5 Performance Evaluation
2.6 Summary
Chapter 3 Information Retrieval Models
3.1 Computational Models For Research
3.2 Dimensionality Reduction With Svd And Pca
3.3 Optimal Information Retrieval With Genetic Algorithm
3.4 Summary
Chapter 4 Hybrid Swarm Intelligence Approaches For Optimal Information Retrieval
4.1 Introduction
4.2 Swarm Intelligence Methods
4.3 Particle Swarm Optimization For Information Retrieval
4.4 Bees Algorithm For Information Retrieval
4.5 Hybrid Pso And Bees Algorithms For Information Retrieval
4.6 Performance Evaluation
4.7 Summary
Chapter 5 Information Retrieval And Semantic Search
5.1 Introduction
5.2 Document-Oriented Search
5.3 Domain Ontology-Based Semantic Search
5.4 Multimedia Information Search
5.5 Relation-Focused Search
5.6 Semantic Analytics
5.7 Mining-Based Search
5.8 Summary
Chapter 6 Ontology Creation Using Clustering Technique
6.1 Introduction
6.2 Wordnet To Calculate Semantic Similarity
6.3 Basics Of Clustering
6.4 Ontology Construction
6.5 Working Of Ontological Mapper
6.6 Summary
Chapter 7 Natural Language Processing For Information Retrieval
7.1 Introduction
7.2 Nlp Techniques For Ir
7.3 Parsing Techniques For Understanding Text Syntax
7.4 Named Entity Recognition (Ner)
7.5 Word Embedding/Vectorization
7.6 Summary
Chapter 8 Deep Learning For Information Retrieval
8.1 Introduction
8.2 Deep Learning Fundamentals
8.3 Deep Learning Approaches For Information Retrieval
8.4 Summary
Chapter 9 Application Of Ontology In Domain-Specific Information Retrieval: A Case Study
9.1 Introduction
9.2 System Architecture
9.3 Keyword Manager (Basic Query Mapper)
9.4 Query Prototype Mapper
9.5 Query Similarity
9.6 Query Word Order Mapper
9.7 Spelling Correction
9.8 Evaluation Process
9.9 Summary
Chapter 10 Applications Of Nlp And Ir
10.1 Introduction
10.2 Prediction Of Likes And Retweets
10.3 Intelligent Question Answering
10.4 Summary
Index