Evaluating Word Sense Induction and Disambiguation Methods.
Word Sense Induction (WSI) is the task of identifying the different uses (senses) of a target word in a given text in an unsupervised manner, i.e. without relying on any external resources such as dictionaries or sense-tagged data. This paper presents a thorough description of the SemEval-2010 WSI t...
| Published in: | Language Resources & Evaluation Vol. 47; no. 3; pp. 579 - 606 |
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| Main Authors: | , |
| Format: | Article |
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Springer Nature
Sep2013
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=90015530&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 90015530 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2013 vid: 47 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 90015530 10.1007/s10579-012-9205-0 ppf: 579 ppct: 27 formats: fmt: @attributes: type: P size: 392KB tig: atl: Evaluating Word Sense Induction and Disambiguation Methods. aug: au: Klapaftis, Ioannis Manandhar, Suresh affil: Microsoft Corporation, Redmond USA Department of Computer Science, University of York, York UK su: Lexical grammar Semantics Comparative grammar Encyclopedias & dictionaries Performance evaluation Language & languages sug: subj: Lexical grammar Semantics Comparative grammar Encyclopedias & dictionaries Performance evaluation Language & languages keyword: Lexical Semantics Word Sense Disambiguation Word Sense Induction ab: Word Sense Induction (WSI) is the task of identifying the different uses (senses) of a target word in a given text in an unsupervised manner, i.e. without relying on any external resources such as dictionaries or sense-tagged data. This paper presents a thorough description of the SemEval-2010 WSI task and a new evaluation setting for sense induction methods. Our contributions are two-fold: firstly, we provide a detailed analysis of the Semeval-2010 WSI task evaluation results and identify the shortcomings of current evaluation measures. Secondly, we present a new evaluation setting by assessing participating systems' performance according to the skewness of target words' distribution of senses showing that there are methods able to perform well above the Most Frequent Sense ( MFS) baseline in highly skewed distributions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2013. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2013 holdings: @attributes: islocal: N |
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