Report on the TREC 2006 Genomics Experiment

This paper describes our participation in the TREC 2006 Genomics evaluation campaign. In an effort to find text passages that will meet user requests, we propose and evaluate a new approach to the generation of orthographic variants of search terms (mainly genomic names in our case). We also evaluate the retrieval effectiveness of both the Okapi (BM25) model and the I(n)B2 probabilistic model derived from the Divergence from Randomness paradigm. In our experiments, we find that in terms of mean average precision the latter model performs clearly better than the Okapi model (with a relative difference of 50%). Moreover when comparing a 5-gram indexing approach to word-based indexing schemes, the mean average precision decreases by about 10% when using the n-gram indexing scheme. Additionally, including the article’s title in all passages generated from a given article does not improve retrieval effectiveness. Finally, the generation of passages delimited by HTML tags was not a success. The performance achieved was in fact rather poor, suggesting that there were too many sentences within such text passages.