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RAPID PATTERN DEVELOPMENT FOR CONCEPT RECOGNITION SYSTEMS: APPLICATION TO POINT MUTATIONS

Department of Biochemistry and Molecular Genetics, Center for Computational Pharmacology, University of Colorado Health Sciences Center, Aurora, CO, USA

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WILLIAM A. BAUMGARTNER, JR.

Center for Computational Pharmacology, University of Colorado Health Sciences Center, Aurora, CO, USA

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DAVID A. RANDOLPH

Department of Computer Science, University of Colorado at Boulder, Boulder, CO, USA

Motorola Mobile Devices, Libertyville, IL, USA

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K. BRETONNEL COHEN

Center for Computational Pharmacology, University of Colorado Health Sciences Center, Aurora, CO, USA

Department of Linguistics, University of Colorado at Boulder, Boulder, CO, USA

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, and

LAWRENCE HUNTER

Center for Computational Pharmacology, University of Colorado Health Sciences Center, Aurora, CO, USA

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https://doi.org/10.1142/S0219720007003144Cited by:7 (Source: Crossref)

Abstract

The primary biomedical literature is being generated at an unprecedented rate, and researchers cannot keep abreast of new developments in their fields. Biomedical natural language processing is being developed to address this issue, but building reliable systems often requires many expert-hours. We present an approach for automatically developing collections of regular expressions to drive high-performance concept recognition systems with minimal human interaction. We applied our approach to develop MutationFinder, a system for automatically extracting mentions of point mutations from the text. MutationFinder achieves performance equivalent to or better than manually developed mutation recognition systems, but the generation of its 759 patterns has required only 5.5 expert-hours. We also discuss the development and evaluation of our recently published high-quality, human-annotated gold standard corpus, which contains 1,515 complete point mutation mentions annotated in 813 abstracts. Both MutationFinder and the complete corpus are publicly available at .

Keywords:

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