A shallow parser based on closed-class words to capture relations in biomedical text

Persistent Link:
http://hdl.handle.net/10150/105844
Title:
A shallow parser based on closed-class words to capture relations in biomedical text
Author:
Leroy, Gondy; Chen, Hsinchun; Martinez, Jesse D.
Citation:
A shallow parser based on closed-class words to capture relations in biomedical text 2003-06, 36:145-158 Journal of Biomedical Informatics
Publisher:
Elsevier
Journal:
Journal of Biomedical Informatics
Issue Date:
Jun-2003
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105844
Submitted date:
2004-08-20
Abstract:
Natural language processing for biomedical text currently focuses mostly on entity and relation extraction. These entities and relations are usually pre-specified entities, e.g., proteins, and pre-specified relations, e.g., inhibit relations. A shallow parser that captures the relations between noun phrases automatically from free text has been developed and evaluated. It uses heuristics and a noun phraser to capture entities of interest in the text. Cascaded finite state automata structure the relations between individual entities. The automata are based on closed-class English words and model generic relations not limited to specific words. The parser also recognizes coordinating conjunctions and captures negation in text, a feature usually ignored by others. Three cancer researchers evaluated 330 relations extracted from 26 abstracts of interest to them. There were 296 relations correctly extracted from the abstracts resulting in 90% precision of the relations and an average of 11 correct relations per abstract.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Artificial Intelligence; Natural Language Processing
Local subject classification:
National Science Digital Library; NSDL; Artificial Intelligence lab; AI lab; Natural language processing; Shallow parsing; Finite state automata; Biomedicine; Free text; Bottom-up parser; NLP

Full metadata record

DC FieldValue Language
dc.contributor.authorLeroy, Gondyen_US
dc.contributor.authorChen, Hsinchunen_US
dc.contributor.authorMartinez, Jesse D.en_US
dc.date.accessioned2004-08-20T00:00:01Z-
dc.date.available2010-06-18T23:35:25Z-
dc.date.issued2003-06en_US
dc.date.submitted2004-08-20en_US
dc.identifier.citationA shallow parser based on closed-class words to capture relations in biomedical text 2003-06, 36:145-158 Journal of Biomedical Informaticsen_US
dc.identifier.urihttp://hdl.handle.net/10150/105844-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractNatural language processing for biomedical text currently focuses mostly on entity and relation extraction. These entities and relations are usually pre-specified entities, e.g., proteins, and pre-specified relations, e.g., inhibit relations. A shallow parser that captures the relations between noun phrases automatically from free text has been developed and evaluated. It uses heuristics and a noun phraser to capture entities of interest in the text. Cascaded finite state automata structure the relations between individual entities. The automata are based on closed-class English words and model generic relations not limited to specific words. The parser also recognizes coordinating conjunctions and captures negation in text, a feature usually ignored by others. Three cancer researchers evaluated 330 relations extracted from 26 abstracts of interest to them. There were 296 relations correctly extracted from the abstracts resulting in 90% precision of the relations and an average of 11 correct relations per abstract.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectArtificial Intelligenceen_US
dc.subjectNatural Language Processingen_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.otherNatural language processingen_US
dc.subject.otherShallow parsingen_US
dc.subject.otherFinite state automataen_US
dc.subject.otherBiomedicineen_US
dc.subject.otherFree texten_US
dc.subject.otherBottom-up parseren_US
dc.subject.otherNLPen_US
dc.titleA shallow parser based on closed-class words to capture relations in biomedical texten_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of Biomedical Informaticsen_US
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