Filling preposition-based templates to capture information from medical abstracts.
Due to the recent explosion of information in the biomedical field, it is hard for a single researcher to review the complex network involving genes, proteins, and interactions. We are currently building GeneScene, a toolkit that will assist researchers in reviewing existing literature, and report on the first phase in our development effort: extracting the relevant information from medical abstracts. We are developing a medical parser that extracts information, fills basic prepositional-based templates, and combines the templates to capture the underlying sentence logic. We tested our parser on 50 unseen abstracts and found that it extracted 246 templates with a precision of 70%. In comparison with many other techniques, more information was extracted without sacrificing precision. Future improvement in precision will be achieved by correcting three categories of errors.
Leroy
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Department of Management Information Systems, University of Arizona, 1030 E. Helen St, Tucson, AZ 85721, USA.
Chen
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D000043:Abstracting and Indexing / Q000379:methods* / Q000592:standards; D016208:Databases, Factual; D005823:Genetics; D011786:Quality Control; D015203:Reproducibility of Results