A Learning Approach to Shallow Parsing

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dc.creatorMuñoz, Marcia
dc.creatorPunyakanok, Vasin
dc.creatorRoth, Dan
dc.creatorZimak, Dav
dc.date2000-08-22
dc.date.accessioned2026-07-07T03:16:29Z
dc.date.available2026-07-07T03:16:29Z
dc.descriptionA SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors to produce a coherent inference. Two instantiations of this approach are studied and experimental results for Noun-Phrases (NP) and Subject-Verb (SV) phrases that compare favorably with the best published results are presented. In doing that, we compare two ways of modeling the problem of learning to recognize patterns and suggest that shallow parsing patterns are better learned using open/close predictors than using inside/outside predictors.
dc.descriptionLaTex 2e, 11 pages, 2 eps figures, 1 bbl file, uses colacl.sty
dc.identifierhttps://arxiv.org/abs/cs/0008022
dc.identifierhttp://arxiv.org/abs/cs/0008022
dc.identifierProceedings of EMNLP-VLC'99, pages 168-178
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30370
dc.subjectMachine Learning
dc.subjectComputation and Language
dc.subjectI.2.6; I.2.7
dc.titleA Learning Approach to Shallow Parsing
dc.typetext

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