Combining Resources to Find Answers to Biomedical Questions

TREC(2007)

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摘要
One of the NLM experimental approaches to the 2007 Genomics track question answering task followed the track evaluation design: we attempted identifying exact answers in the form of semantic relations between biomedical entities named in questions and the potential answer types and then marked the passages containing the relations as containing the answers. The goal of this knowledge- based approach was to improve the answer precision. To boost recall, evidence obtained through relation extraction was combined with passage scores obtained by semantic filtering and passage retrieval. Our second approach, the fusion of retrieval results of several search engines established to be reliably successful in the past, was used as the baseline, which ultimately was not improved upon by the knowledge-based approach. The impact of the relevance of whole documents on finding passages containing answers was tested in the third approach, an interactive retrieval experiment, in which the relevance of a document was determined by virtue of its retrieval in an expert PubMed® search and an occasional examination of its abstract. This relatively moderately labor-intensive approach significantly improved the fusion retrieval results.
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关键词
thematic analysis.,medline/pubmed,machine learning,statistical natural language processing,genomics,mesh,search engine,relation extraction,knowledge base,question answering
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