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Title
Topic Level Disambiguation for Weak Queries
Author(s)
Hui ZhangKiduk YangElin Jacob
Publication Year
2013-09-30
Abstract
Despite limited success, today's information retrieval (IR) systems are not intelligent or reliable. IR systems return poor search results when users formulate their information needs into incomplete or ambiguous queries (i.e., weak queries). Therefore, one of the main challenges in modern IR research is to provide consistent results across all queries by improving the performance on weak queries. However, existing IR approaches such as query expansion are not overly effective because they make little effort to analyze and exploit the meanings of the queries. Furthermore, word sense disambiguation approaches, which rely on textual context, are ineffective against weak queries that are typically short. Motivated by the demand for a robust IR system that can consistently provide highly accurate results, the proposed study implemented a novel topic detection that leveraged both the language model and structural knowledge of Wikipedia and systematically evaluated the effect of query disambiguation and topic-based retrieval approaches on TREC collections. The results not only confirm the effectiveness of the proposed topic detection and topic-based retrieval approaches but also demonstrate that query disambiguation does not improve IR as expected.
Keyword
Topic Detection; Query Disambiguation; Language Model; Information Retrieval; Natural Language Processing
Journal Title
Journal of Information Science Theory and Practice
Citation Volume
1
ISSN
2287-4577
DOI
10.1633/JISTaP.2013.1.3.3
Files in This Item:
Thumbnail E1JSCH_2013_v1n3_33.pdf264.07 kBDownload
Appears in Collections:
8. KISTI 간행물 > JISTaP > Vol. 1 - No. 3
Type
Article
URI
https://repository.kisti.re.kr/handle/10580/8638
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