Proximity Relevance Model for Query Expansion

Liana Ermakova (1) , Josiane Mothe , Elena Nikitina

1 HCTI - Héritages et Constructions dans le Texte et l'Image

Query expansion (QE) aims at improving information retrieval effectiveness by enhancing the query formulation. Because users' queries are generally short and because of the language ambiguity, some information needs are difficult to satisfy. Query reformulation and QE methods have been developed to face this issue. Pseudo relevance feedback (PRF) considers the top retrieved documents as relevant and uses their content in order to expand the initial query. Rather than considering feedback documents as a bag of words, it is possible to exploit term proximity information. Although there are some researches in this direction, the majority of them is empirical. The lack of theoretical works in this area motivated us to introduce a novel method integrated into the language model formalism that takes advantage of the remoteness of candidate terms for QE from query terms within feedback documents. In contrast to previous works, our approach captures the proximity directly and in terms of sentences rather than tokens. We show that the method significantly improves the retrieval performance on TREC collections especially for difficult queries.

Mots clés: Information Retrieval; Query Expansion; Pseudo Relevance Feedback; Term Proximity; Language Model; Relevance Model

Domaines: Littératures

Liana Ermakova, Josiane Mothe, Elena Nikitina. Proximity Relevance Model for Query Expansion. ACM Symposium on Applied Computing (SAC), 2016, pp.1-10. ⟨hal-03006405⟩