Information retrieval (IR) can be defined as the process of representing, managing, searching, retrieving, and presenting information. Good IR involves understanding information needs and interests, developing an effective search technique, system, presentation, distribution and delivery. The increased use of the Web and wider availability of information in this environment led to the development of Web search engines. This change has brought fresh challenges to a wider variety of users needs, tasks, and types of information.
Today search engines are seen in enterprises, on laptops, in individual websites, in library catalogues, and elsewhere. Information Retrieval: Searching in the 21st Century
Information Retrieval Focuses on:
Information Retrieval Models
User–centred Evaluation of Information Retrieval Systems
Multimedia Resource Discovery
Image Users Needs and Searching Behaviour
Web Information Retrieval
Context and Information Retrieval
Text Categorisation and Genre in Information Retrieval
The Role of Natural Language Processing in Information Retrieval: Search for Meaning and Structure
Cross–language Information Retrieval
Performance Issues in Parallel Computing for Information Retrieval
This book is an invaluable reference for graduate students on IR courses or courses in related disciplines (e.g. computer science, information science, human–computer interaction, and knowledge management), academic and industrial researchers, and industrial personnel tracking information search technology developments to understand the business implications. Intermediate–advanced level undergraduate students on IR or related courses will also find this text insightful. Chapters are supplemented with exercises to stimulate further thinking.
About the Editors.
List of Contributors.
1 Information Retrieval Models (Djoerd Hiemstra).
1.2 Exact Match Models.
1.3 Vector Space Approaches.
1.4 Probabilistic Approaches.
1.5 Summary and Further Reading.
2 User–centred Evaluation of Information Retrieval Systems (Pia Borlund).
2.2 The MEDLARS Test.
2.3 The Okapi Project.
2.4 The Interactive IR Evaluation Model.
3 Multimedia Resource Discovery (Stefan Rüger).
3.2 Basic Multimedia Search Technologies.
3.3 Challenges of Automated Visual Indexing.
3.4 Added Services.
3.5 Browsing: Lateral and Geotemporal.
4 Image Users Needs and Searching Behaviour (Stina Westman).
4.2 Image Attributes and Users Needs.
4.3 Image Searching Behaviour.
4.4 New Directions for Image Access.
5 Web Information Retrieval (Nick Craswell and David Hawking).
5.2 Distinctive Characteristics of the Web.
5.3 Three Ranking Problems.
5.4 Other Web IR Issues.
5.5 Evaluation of Web Search Effectiveness.
6 Mobile Search (David Mountain, Hans Myrhaug and Aye Göker).
6.1 Introduction: Mobile Search Why Now?
6.2 Information for Mobile Search.
6.3 Designing for Mobile Search.
6.4 Case Studies.
7 Context and Information Retrieval (Aye Göker, Hans Myrhaug and Ralf Bier).
7.2 What is Context?
7.3 Context in Information Retrieval.
7.4 Context Modelling and Representation.
7.5 Context and Content.
7.6 Related Topics.
7.7 Evaluating Context–aware IR Systems.
8 Text Categorisation and Genre in Information Retrieval (Stuart Watt).
8.1 Introduction: What is Text Categorisation?
8.2 How to Build a Text Categorisation System.
8.3 Evaluating Text Categorisation Systems.
8.4 Genre: Text Structure and Purpose.
8.5 Related Techniques: Information Filtering.
8.6 Applications of Text Categorisation.
8.7 Summary and the Future of Text Categorisation.
9 Semantic Search (John Davies, Alistair Duke and Atanas Kiryakov).
9.2 Semantic Web.
9.3 Metadata and Annotations.
9.4 Semantic Annotations: the Fibres of the Semantic Web.
9.5 Semantic Annotation of Named Entities.
9.6 Semantic Indexing and Retrieval.
9.7 Semantic Search Tools.
10 The Role of Natural Language Processing in Information Retrieval: Searching for Meaning and Structure (Tony Russell–Rose and Mark Stevenson).
10.2 Natural Language Processing Techniques.
10.3 Applications of Natural Language Processing in Information Retrieval.
11 Cross–Language Information Retrieval (Daqing He and Jianqiang Wang).
11.2 Major Approaches and Challenges in CLIR.
11.3 Identifying Translation Units.
11.4 Obtaining Translation Knowledge.
11.5 Using Translation Knowledge.
11.6 Interactivity in CLIR.
11.7 Evaluation of CLIR Systems.
11.8 Summary and Future Directions.
12 Performance Issues in Parallel Computing for Information Retrieval (Andrew MacFarlane).
12.2 Why Parallel IR?
12.3 Review of Previous Work.
12.4 Distribution Methods for Inverted File Data.
12.5 Tasks in Information Retrieval.
12.6 A Synthetic Model of Performance for Parallel Information Retrieval.
12.7 Empirical Examination of Synthetic Model.
12.8 Summary and Further Research.
Solutions to Exercises.
I would recommend Information Retrieval to readers who already have a base of knowledge on core IR concepts." (Inf Retrieval, 9 December 2010)"The authors have definitely met the challenge of providing a comprehensive volume of factual knowledge on IR fundamentals. I highly recommend the book to those in both academia and industry." (Computing Reviews, 9 May 2011)