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Filling Preposition-based Templates To Capture Information from Medical Abstracts

Leroy, Gondy and Chen, Hsinchun (2002) Filling Preposition-based Templates To Capture Information from Medical Abstracts. In Proceedings Pacific Symposium on Biocomputing, pages pp. 350-361, Kaua'i, HI.

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Abstract

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.

EPrint Type:Conference Paper
Keywords:National Science Digital Library, NSDL, Artificial Intelligence Lab, AI Lab, GeneScene
Subjects:Medical Libraries
Information Extraction
ID Code:438
Deposited On:20 August 2004
Alternative Locations:http://ai.bpa.arizona.edu/go/papers.html
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