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|Title:||Mining dependency relations for query expansion in passage retrieval||Authors:||Sun, R.
|Issue Date:||2006||Citation:||Sun, R.,Ong, C.-H.,Chua, T.-S. (2006). Mining dependency relations for query expansion in passage retrieval. Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2006 : 382-389. ScholarBank@NUS Repository.||Abstract:||Classical query expansion techniques such as the local context analysis (LCA) make use of term co-occurrence statistics to incorporate additional contextual terms for enhancing passage retrieval. However, relevant contextual terms do not always cooccur frequently with the query terms and vice versa. Hence the use of such methods often brings in noise, which leads to reduced precision. Previous studies have demonstrated the importance of relationship analysis for natural language queries in passage retrieval. However, they found that without query expansion, the performance is not satisfactory for short queries. In this paper, we present two novel query expansion techniques that make use of dependency relation analysis to extract contextual terms and relations from external corpuses. The techniques are used to enhance the performance of density based and relation based passage retrieval frameworks respectively. We compare the performance of the resulting systems with LCA in a density based passage retrieval system (DBS) and a relation based system without any query expansion (RBS) using the factoid questions from the TREC-12 QA task. The results show that in terms of MRR scores, our relation based term expansion method with DBS outperforms the LCA by 9.81%, while our relation expansion method outperforms RBS by 17.49%. Copyright 2006 ACM.||Source Title:||Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval||URI:||http://scholarbank.nus.edu.sg/handle/10635/41623||ISBN:||1595933697|
|Appears in Collections:||Staff Publications|
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