DEV-JOURNAL OF INFORMATION SCIENCE THEORY AND PRACTICE

Abstract

Despite limited success, today’s information retrieval (IR) systems are not intelligent and 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 leverage both 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 confirm the effectiveness of proposed query segmentation and topic-based retrieval approaches but also demonstrate that query disambiguation does not improve IR as expected.

Abstract

Abstract