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Cited 1 time in webofscience Cited 2 time in scopus
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dc.contributor.authorBai, X.-M-
dc.contributor.authorLi, J.-J-
dc.contributor.authorKim, D.-I-
dc.contributor.authorLee, J.-H.-
dc.date.accessioned2017-07-19T12:31:27Z-
dc.date.available2017-07-19T12:31:27Z-
dc.date.created2014-03-11-
dc.date.issued2006-12-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/35967-
dc.description.abstractIn general, there are two types of noun phrases (NP): Base Noun Phrase (BNP), and Maximal-Length Noun Phrase (MNP). MNP identification can largely reduce the complexity of full parsing, help analyze the general structure of complex sentences, and provide important clues for detecting main predicates in Chinese sentences. In this paper, we propose a 2-phase hybrid approach for MNP identification which adopts salient features such as expanded chunks and classified punctuations to improve performance. Experimental result shows a high quality performance of 89.66% in F-1-measure.-
dc.languageEnglish-
dc.publisherSpringer-
dc.relation.isPartOfLECTURE NOTES IN ARTIFICIAL INTELLIGENCE (ICCPOL2006)-
dc.titleIdentification of Maximal-Length Noun Phrases Based on Expanded Chunks and Classified Punctuations in Chinese-
dc.typeArticle-
dc.identifier.doi10.1007/11940098_28-
dc.type.rimsART-
dc.identifier.bibliographicCitationLECTURE NOTES IN ARTIFICIAL INTELLIGENCE (ICCPOL2006), v.4285, pp.268 - 276-
dc.identifier.wosid000244584200028-
dc.date.tcdate2019-03-01-
dc.citation.endPage276-
dc.citation.startPage268-
dc.citation.titleLECTURE NOTES IN ARTIFICIAL INTELLIGENCE (ICCPOL2006)-
dc.citation.volume4285-
dc.contributor.affiliatedAuthorLee, J.-H.-
dc.identifier.scopusid2-s2.0-77049101743-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc1-
dc.description.scptc2*
dc.date.scptcdate2018-05-121*
dc.description.isOpenAccessN-
dc.type.docTypeProceedings Paper-
dc.subject.keywordAuthorMaximal-Length Noun Phrase (MNP)-
dc.subject.keywordAuthorexpanded chunk-
dc.subject.keywordAuthorclassified punctuation-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-

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이종혁LEE, JONG HYEOK
Grad. School of AI
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