Capturing and measuring thematic relatedness.
In this paper we explain the difference between two aspects of semantic relatedness: taxonomic and thematic relations. We notice the lack of evaluation tools for measuring thematic relatedness, identify two datasets that can be recommended as thematic benchmarks, and verify them experimentally. In f...
| Publicado en: | Language Resources & Evaluation Vol. 54; no. 3; pp. 645 - 683 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Sep2020
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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=144950766&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 144950766 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2020 vid: 54 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 144950766 10.1007/s10579-019-09452-w ppf: 645 ppct: 38 formats: fmt: – @attributes: type: T – @attributes: type: P size: 842KB tig: atl: Capturing and measuring thematic relatedness. aug: au: Kacmajor, Magdalena Kelleher, John D. affil: Innovation Exchange, IBM Ireland, Dublin, Ireland ADAPT Centre and ICE Research Institute, Technological University Dublin, Dublin, Ireland su: Measuring instruments Information resources Relatedness (Psychology) sug: subj: Measuring instruments Information resources Relatedness (Psychology) keyword: Evaluation datasets Semantic relatedness Thematic relations Word vector representations ab: In this paper we explain the difference between two aspects of semantic relatedness: taxonomic and thematic relations. We notice the lack of evaluation tools for measuring thematic relatedness, identify two datasets that can be recommended as thematic benchmarks, and verify them experimentally. In further experiments, we use these datasets to perform a comprehensive analysis of the performance of an extensive sample of computational models of semantic relatedness, classified according to the sources of information they exploit. We report models that are best at each of the two dimensions of semantic relatedness and those that achieve a good balance between the two. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2020. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2020 holdings: @attributes: islocal: N |
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