Show simple item record

dc.contributor.authorOng, Thian-Huat
dc.contributor.authorChen, Hsinchun
dc.date.accessioned2004-08-17T00:00:01Z
dc.date.available2010-06-18T23:21:42Z
dc.date.issued1999en_US
dc.date.submitted2004-08-17en_US
dc.identifier.citationUpdateable PAT-Tree Approach to Chinese Key Phrase Extraction using Mutual Information: A Linguistic Foundation for Knowledge Management 1999, :63-84en_US
dc.identifier.urihttp://hdl.handle.net/10150/105216
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractThere has been renewed research interest in using the statistical approach to extraction of key phrases from Chinese documents because existing approaches do not allow online frequency updates after phrases have been extracted. This consequently results in inaccurate, partial extraction. In this paper, we present an updateable PAT-tree approach. In our experiment, we compared our approach with that of Lee-Feng Chien with that showed an improvement in recall from 0.19 to 0.43 and in precision from 0.52 to 0.70. This paper also reviews the requirements for a data structure that facilitates implementation of any statistical approaches to key-phrase extraction, including PATtree, PAT-array and suffix array with semi-infinite strings.
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.subjectKnowledge Managementen_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.otherPAT-treeen_US
dc.titleUpdateable PAT-Tree Approach to Chinese Key Phrase Extraction using Mutual Information: A Linguistic Foundation for Knowledge Managementen_US
dc.typeConference Paperen_US
refterms.dateFOA2018-08-21T10:47:28Z
html.description.abstractThere has been renewed research interest in using the statistical approach to extraction of key phrases from Chinese documents because existing approaches do not allow online frequency updates after phrases have been extracted. This consequently results in inaccurate, partial extraction. In this paper, we present an updateable PAT-tree approach. In our experiment, we compared our approach with that of Lee-Feng Chien with that showed an improvement in recall from 0.19 to 0.43 and in precision from 0.52 to 0.70. This paper also reviews the requirements for a data structure that facilitates implementation of any statistical approaches to key-phrase extraction, including PATtree, PAT-array and suffix array with semi-infinite strings.


Files in this item

Thumbnail
Name:
ong.pdf
Size:
272.5Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record