Expanding Queries Using Multiple Resources (The AID Group at TREC 2006: Genomics Track)

We describe our participation in the TREC 2006 Genomics track, in which our main focus was on query expansion. We hypothe- sized that applying query expansion techniques would help us both to identify and retrieve syn- onymous terms, and to cope with ambiguity. To this end, we developed several collection-specific as well as web-based strategies. We also per- formed post-submission experiments, in which we compare various retrieval engines, such as Lucene, Indri, and Lemur, using a simple baseline topic- set. When indexing entire paragraphs as pseudo- documents, we find that Lemur is able to achieve the highest document-, passage-, and aspect-level scores, using the KL-divergence method and its default settings. Additionally, we index the col- lection at a lower level of granularity, by creating pseudo-documents comprising of individual sen- tences. When we search these instead of para- graphs in Lucene, the passage-level scores im- prove considerably. Finally we note that stemming improves overall scores by at least 10%.