Hierarchical Decision Lists for Word Sense Disambiguation.
This paper describes a supervised algorithm for word sense disambiguation based on hierarchies of decision lists. This algorithm supports a useful degree of conditional branching while minimizing the training data fragmentation typical of decision trees. Classifications are based on a rich set of co...
| Publicado en: | Computers & the Humanities Vol. 34; no. 1/2; pp. 179 - 187 |
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| Formato: | Artículo |
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Springer Nature
Apr2000
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=16898908&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 16898908 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00104817 CHM jtl: Computers & the Humanities issn: 00104817 maglogo: N pubinfo: dt: Apr2000 vid: 34 iid: 1/2 pid: 237 pub: Springer Nature artinfo: ui: 16898908 10.1023/A:1002674829964 ppf: 179 ppct: 8 formats: fmt: @attributes: type: P size: 40KB tig: atl: Hierarchical Decision Lists for Word Sense Disambiguation. aug: au: Yarowsky, David affil: Dept. of Computer Science and Center for Language and Speech Processing, Johns Hopkins University, Baltimore, MD 21218, USA su: Ambiguity Semantics English language education Language & logic Comparative linguistics Comparative grammar sug: subj: Ambiguity Semantics English language education Language & logic Comparative linguistics Comparative grammar keyword: decision lists lexical ambiguity resolution SENSEVAL supervised machine learning word sense disambiguation ab: This paper describes a supervised algorithm for word sense disambiguation based on hierarchies of decision lists. This algorithm supports a useful degree of conditional branching while minimizing the training data fragmentation typical of decision trees. Classifications are based on a rich set of collocational, morphological and syntactic contextual features, extracted automatically from training data and weighted sensitive to the nature of the feature and feature class. The algorithm is evaluated comprehensively in the SENSEVAL framework, achieving the top performance of all participating supervised systems on the 36 test words where training data is available. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Computers & the Humanities is a copyright of Springer, 2000. All Rights Reserved. item: Computers & the Humanities holder: Springer Nature dt: @attributes: year: 2000 holdings: @attributes: islocal: N |
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