Ranking Biomedical Passages for Relevance and Diversity: University of Wisconsin, Madison at TREC Genomics 2006

We report on the University of Wisconsin, Madison’s experience in the TREC Genomics 2006 track, which asks participants to retrieve passages from scientific articles that satisfy biologists’ information needs. An emphasis is placed on returning relevant passages that discuss different aspects of the topic. Using an off-the-shelf information retrieval (IR) engine, we focused on query generation and reranking query results to encourage relevance and diversity. For query generation, we automatically identify noun phrases from the topic descriptions, and use online resources to gather synonyms as expansion terms. Our first submission uses the baseline IR engine results. We rerank the passages using a näıve clustering-based approach in our second run, and we test GRASSHOPPER, a novel graph-theoretic algorithm based on absorbing random walks, in our third run. While our aspect-level results appear to compare favorably with other participants’ on average, our query generation techniques failed to produce adequate query results for several topics, causing our passage and document-level evaluation scores to suffer. Furthermore, we surprisingly achieved higher aspect-level scores using the initial ranking than our methods aimed specifically at promoting diversity. While this sounds discouraging, we have several ideas as to why this happened and hope to produce new methods that correct these shortcomings.