Topic Level Disambiguation for Weak Queries

TL;DR

This paper introduces a novel topic detection method leveraging Wikipedia's structure and language models to improve IR performance on weak, ambiguous queries, showing promising results over traditional disambiguation techniques.

Contribution

The study presents a new topic detection approach that combines language models and Wikipedia knowledge to enhance query disambiguation and retrieval accuracy for weak queries.

Findings

Topic detection improves IR results on weak queries.

Query disambiguation alone does not significantly enhance IR performance.

Wikipedia-based topic modeling outperforms traditional methods.

Abstract

Despite limited success, information retrieval (IR) systems today 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.