DMCA
Conceptual Retrieval based on Feature Clustering of Documents (2002)
| Venue: | Proceedings of ACM SIGIR Workshop on Mathematics/Formal Methods in Information Retrieval |
| Citations: | 3 - 0 self |
BibTeX
@INPROCEEDINGS{Chang02conceptualretrieval,
author = {Youjin Chang and Ikkyu Choi and Jongpill Choi and Minkoo Kim and Vijay V. Raghavan},
title = {Conceptual Retrieval based on Feature Clustering of Documents},
booktitle = {Proceedings of ACM SIGIR Workshop on Mathematics/Formal Methods in Information Retrieval},
year = {2002}
}
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Abstract
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.
Keyphrases
conceptual retrieval query concept feature clustering relevance feedback initial query information need retrieved document web search retrieval performance main problem automatic method many approach document space primitive concept expanded query query expansion correlated term term-term correlation term correlation trec collection relevance information relevance judgment
