Preface | p. ix |
Contributing Authors | p. xiii |
Combining Approaches to Information Retrieval | p. 1 |
Introduction | p. 1 |
Combining Representations | p. 5 |
Combining Queries | p. 9 |
Combining Ranking Algorithms | p. 11 |
Combining Search Systems | p. 13 |
Combining Belief | p. 16 |
Language Models | p. 25 |
Conclusion | p. 28 |
The Use of Exploratory Data Analysis in Information Retrieval Research | p. 37 |
Introduction | p. 37 |
Exploratory Data Analysis | p. 39 |
Weight of Evidence | p. 40 |
Analysis of the Relationship between Document Frequency and the Weight of Evidence of Term Occurrence | p. 43 |
Probabilistic Modeling of Multiple Sources of Evidence | p. 53 |
Conclusions | p. 70 |
Language Models for Relevance Feedback | p. 73 |
Introduction | p. 73 |
The Language Modeling Approach to IR | p. 75 |
Related Work | p. 78 |
Query Expansion in the Language Modeling Approach | p. 83 |
Discussion and Future Work | p. 92 |
Topic Detection and Tracking: Event Clustering as a Basis for First Story Detection | p. 97 |
Topic Detection and Tracking | p. 98 |
On-line Clustering Algorithms | p. 103 |
Experimental Setting | p. 108 |
Event Clustering | p. 110 |
First Story Detection | p. 112 |
Discussion of First Story Detection | p. 119 |
Conclusion | p. 120 |
Future Work | p. 122 |
Distributed Information Retrieval | p. 127 |
Introduction | p. 127 |
Multi-Database Testbeds | p. 129 |
Resource Description | p. 130 |
Resource Selection | p. 131 |
Merging Document Rankings | p. 135 |
Acquiring Resource Descriptions | p. 137 |
Summary and Conclusions | p. 145 |
Topic-Based Language Models for Distributed Retrieval | p. 151 |
Introduction | p. 152 |
Topic Models | p. 154 |
K-Means Clustering | p. 155 |
Four Methods of Distributed Retrieval | p. 155 |
Experimental Setup | p. 158 |
Global Clustering | p. 160 |
Recall-based Retrieval | p. 163 |
Distributed Retrieval in Dynamic Environments | p. 165 |
More Clusters | p. 165 |
Better Choice of Initial Clusters | p. 165 |
Local Clustering | p. 166 |
Multiple-Topic Representation | p. 166 |
Efficiency | p. 168 |
Related Work | p. 168 |
Conclusion and Future Work | p. 169 |
The Effect of Collection Organization and Query Locality on Information Retrieval System Performance | p. 173 |
Introduction | p. 174 |
Related Work | p. 176 |
System Architectures | p. 181 |
Configuration with Respect to Collection Organization, Collection Access Skew, and Query Locality | p. 185 |
Simulation Model | p. 188 |
Experiments | p. 189 |
Conclusions | p. 197 |
Cross-Language Retrieval via Transitive Translation | p. 203 |
Introduction | p. 203 |
Translation Resources | p. 205 |
Dictionary Translation and Ambiguity | p. 208 |
Resolving Ambiguity | p. 209 |
Addressing Limited Resources | p. 212 |
Summary | p. 230 |
Building, Testing, and Applying Concept Hierarchies | p. 235 |
Introduction | p. 235 |
Building a Concept Hierarchy | p. 238 |
Presenting a Concept Hierarchy | p. 246 |
Evaluating the Structures | p. 251 |
Future Work | p. 255 |
Conclusions | p. 261 |
ANOVA analysis | p. 262 |
Appearance-Based Global Similarity Retrieval of Images | p. 267 |
Introduction | p. 268 |
Appearance Related Representations | p. 272 |
Computing Global Appearance Similarity | p. 278 |
Trademark Retrieval | p. 293 |
Conclusions and Limitations | p. 299 |
Index | p. 305 |
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