Information Retrieval: Searching in the 21st Century / Edition 1
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Information Retrieval: Searching in the 21st Century / Edition 1

4.0 1
by Ayse Goker
     
 

ISBN-10: 0470027622

ISBN-13: 9780470027622

Pub. Date: 12/30/2009

Publisher: Wiley

This book is an essential reference to cutting-edge issues and future directions in information retrieval

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,

Overview

This book is an essential reference to cutting-edge issues and future directions in information retrieval

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 focuses on core concepts, and current trends in the field.

This book focuses on:

  • Information Retrieval Models
  • User-centred Evaluation of Information Retrieval Systems
  • Multimedia Resource Discovery
  • Image Users’ Needs and Searching Behaviour
  • Web Information Retrieval
  • Mobile Search
  • Context and Information Retrieval
  • Text Categorisation and Genre in Information Retrieval
  • Semantic Search
  • 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.

Product Details

ISBN-13:
9780470027622
Publisher:
Wiley
Publication date:
12/30/2009
Pages:
295
Product dimensions:
6.80(w) x 9.80(h) x 0.90(d)

Related Subjects

Table of Contents

Foreword.

Preface.

About the Editors.

List of Contributors.

Introduction.

1 Information Retrieval Models (Djoerd Hiemstra).

1.1 Introduction.

1.2 Exact Match Models.

1.3 Vector Space Approaches.

1.4 Probabilistic Approaches.

1.5 Summary and Further Reading.

Exercises.

References.

2 User-centred Evaluation of Information Retrieval Systems (Pia Borlund).

2.1 Introduction.

2.2 The MEDLARS Test.

2.3 The Okapi Project.

2.4 The Interactive IR Evaluation Model.

2.5 Summary.

Exercises.

References.

3 Multimedia Resource Discovery (Stefan Rüger).

3.1 Introduction.

3.2 Basic Multimedia Search Technologies.

3.3 Challenges of Automated Visual Indexing.

3.4 Added Services.

3.5 Browsing: Lateral and Geotemporal.

3.6 Summary.

Exercises.

References.

4 Image Users’ Needs and Searching Behaviour (Stina Westman).

4.1 Introduction.

4.2 Image Attributes and Users’ Needs.

4.3 Image Searching Behaviour.

4.4 New Directions for Image Access.

4.5 Summary.

Exercises.

References.

5 Web Information Retrieval (Nick Craswell and David Hawking).

5.1 Introduction.

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.

5.6 Summary.

Exercises.

References.

6 Mobile Search (David Mountain, Hans Myrhaug and Ayşe Göker).

6.1 Introduction: Mobile Search – Why Now?

6.2 Information for Mobile Search.

6.3 Designing for Mobile Search.

6.4 Case Studies.

6.5 Summary.

Exercises.

References.

7 Context and Information Retrieval (Ayşe Göker, Hans Myrhaug and Ralf Bier).

7.1 Introduction.

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.

7.8 Summary.

Exercises.

References.

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.

Exercises.

References.

9 Semantic Search (John Davies, Alistair Duke and Atanas Kiryakov).

9.1 Introduction.

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.

9.8 Summary.

Exercises.

References.

10 The Role of Natural Language Processing in Information Retrieval: Searching for Meaning and Structure (Tony Russell-Rose and Mark Stevenson).

10.1 Introduction.

10.2 Natural Language Processing Techniques.

10.3 Applications of Natural Language Processing in Information Retrieval.

10.4 Discussion.

10.5 Summary.

Exercises.

References.

11 Cross-Language Information Retrieval (Daqing He and Jianqiang Wang).

11.1 Introduction.

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.

Exercises.

References.

12 Performance Issues in Parallel Computing for Information Retrieval (Andrew MacFarlane).

12.1 Introduction.

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.

Exercises.

References.

Solutions to Exercises.

Index.

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Information Retrieval: Searching in the 21st Century 4 out of 5 based on 0 ratings. 1 reviews.
Boudville More than 1 year ago
This text is a good explication of the current state of information retrieval. Unsurprisingly the discussion is dominated by the seminal case of the Web. As several chapters relate, prior to the Web this field was dominated by the case where a user [=searcher] would typically hand a query to a specialist [usually a librarian], and the latter would craft an actual query that was input to an IR database. Also, the database was built and maintained by experts. Much of the book describes how the Web differs; a harder case to treat, not least because of the sheer size of the Web corpus. One tough problem is tackled in a chapter on multimedia resource discovery. How to search across a corpus of images or video for desired data? This is more difficult than a search of text or HTML files. In turn, this problem leads to largely unresolved subproblems, like the semantic gap. The latter refers to the distance in meaning between scanning an image to extract low level data about its pixels' colours and distribution, and the higher level understanding of what the image is about, in human cognitive terms. Barring breakthroughs in artificial intelligence, these problems may be unresolvable. The text shows that current solutions are approximations. One of which is the manual tagging of images and video, so that the tags can be searched as text. Another is at the GUI level, where the searcher can input an image and searching is then done for similar images. A feedback loop is made by the searcher being able to pick an image out of the result set of images, for another iteration of search. Unfortunately, this chapter has some example images that are too small for the reader to easily discern. A simple improvement would have been to expand the images, at the cost of a few extra pages. Other chapters discuss different challenges. Like picking spam out of a set of email. Existing antispam methods are only briefly discussed, but you get the gist of several ideas. Cross language IR is the final chapter. Machine translation is shown to be still quite rough, but it is the only realistic way of analysing huge data sets on the Web. Manual translations are more accurate but simply too slow and costly. We see that MT is greatly improved if there are existing Rosetta stones - pages in 2 languages that have been written to mean the same thing. From this, MT can bootstrap to greatly improve the automated translation of other documents between those languages.