Filling Preposition-based Templates To Capture Information from Medical Abstracts

Persistent Link:
http://hdl.handle.net/10150/105077
Title:
Filling Preposition-based Templates To Capture Information from Medical Abstracts
Author:
Leroy, Gondy; Chen, Hsinchun
Citation:
Filling Preposition-based Templates To Capture Information from Medical Abstracts 2002, :350-361
Issue Date:
2002
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105077
Submitted date:
2004-08-20
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.
Type:
Conference Paper
Language:
en
Keywords:
Medical Libraries; Information Extraction
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab; GeneScene

Full metadata record

DC FieldValue Language
dc.contributor.authorLeroy, Gondyen_US
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-08-20T00:00:01Z-
dc.date.available2010-06-18T23:18:52Z-
dc.date.issued2002en_US
dc.date.submitted2004-08-20en_US
dc.identifier.citationFilling Preposition-based Templates To Capture Information from Medical Abstracts 2002, :350-361en_US
dc.identifier.urihttp://hdl.handle.net/10150/105077-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractDue 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.subjectMedical Librariesen_US
dc.subjectInformation Extractionen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherGeneSceneen_US
dc.titleFilling Preposition-based Templates To Capture Information from Medical Abstractsen_US
dc.typeConference Paperen_US
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