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Conceptual Retrieval based on Feature Clustering of Documents (2002)
| Content Provider | CiteSeerX |
|---|---|
| Author | Chang, Youjin Choi, Ikkyu Choi, Jongpill Kim, Minkoo Raghavan, Vijay V. |
| Description | Proceedings of ACM SIGIR Workshop on Mathematics/Formal Methods in Information Retrieval In the Web search, since users' queries usually consist of only a few words, it is hard to identify their information needs. To solve this problem, many approaches have tried to expand initial queries and to reweight the terms in the expanded queries using users' relevance judgments. Although relevance feedback is most effective when relevance information about retrieved documents is provided by users, it is not a fully automatic method. Another solution is to use correlated terms for query expansion. The main problem with this approach is how to construct the term-term correlations that can be used effectively to improve retrieval performance. In this study, we try to construct query concepts that denote users' information needs from a document space, rather than to reformulate initial queries using the term correlations and/or users' relevance feedback. To make query concepts, we extract features from each document, and cluster the features into primitive concepts that are used to form query concepts. Experiment is performed on a TREC collection. |
| File Format | |
| Language | English |
| Publisher Date | 2002-01-01 |
| Access Restriction | Open |
| Subject Keyword | Primitive Concept Query Expansion Many Approach Expanded Query Relevance Judgment Retrieval Performance Conceptual Retrieval Correlated Term Relevance Feedback Retrieved Document Term Correlation Term-term Correlation Information Need Trec Collection Relevance Information Query Concept Document Space Main Problem Feature Clustering Web Search Automatic Method Initial Query |
| Content Type | Text |
| Resource Type | Article |
