Abstract
Dictionary-based biological concept extraction is still the state-of-the-art approach to large-scale biomedical literature annotation and indexing. The exact dictionary lookup is a very simple approach, but always achieves low extraction recall because a biological term often has many variants while a dictionary is impossible to collect all of them. We propose a generic extraction approach, referred to as approximate dictionary lookup, to cope with term variations and implement it as an extraction system called MaxMatcher. The basic idea of this approach is to capture the significant words instead of all words to a particular concept. The new approach dramatically improves the extraction recall while maintaining the precision. In a comparative study on GENIA corpus, the recall of the new approach reaches a 57% recall while the exact dictionary lookup only achieves a 26% recall.
This research work is supported in part from the NSF Career grant (NSF IIS 0448023). NSF CCF 0514679 and the research grant from PA Dept of Health.
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Zhou, X., Zhang, X., Hu, X. (2006). MaxMatcher: Biological Concept Extraction Using Approximate Dictionary Lookup. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_150
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DOI: https://doi.org/10.1007/978-3-540-36668-3_150
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