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Abstract

Sentence retrieval for extracting biomedical knowledge At present, the majority of biomedical Information Retrieval tools process abstracts rather than full-text articles. The increasing availability of full text will allow more knowledge to be extracted with greater reliability. The first step of this is to extract sentences and passages from the text which report scientific results. We investigate the challenges of sentence retrieval, using an annotated corpus of articles cited in a Molecular Interaction Map (Kohn, 1999) developed by McIntosh and Curran (2007). From the annotated facts we generate keywords for sentence retrieval, and analyse the impact of various query relaxation strategies on performance. We also investigate the impact of hedging and commitment in the reporting of scientific results on retrieval. Finally, we look at whether linguistic properties such as anaphora and negation have an impact on retrieval performance. 1

Keyphrases

sentence retrieval annotated fact full text linguistic property report scientific result retrieval performance annotated corpus first step biomedical knowledge molecular interaction map full-text article scientific result biomedical information retrieval various query relaxation strategy process abstract