AGREE: a new benchmark for the evaluation of distributional semantic models of ancient Greek.

The last years have seen the application of Natural Language Processing, in particular, language models, to the study of the Semantics of ancient Greek, but only a little work has been done to create gold data for the evaluation of such models. In this contribution we introduce AGREE, the first benc...

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Publicado en:Digital Scholarship in the Humanities Vol. 39; no. 1; pp. 373 - 393
Autores principales: Stopponi, Silvia, Peels-Matthey, Saskia, Nissim, Malvina
Formato: Artículo
Publicado: Oxford University Press / USA Apr2024
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: AGREE: a new benchmark for the evaluation of distributional semantic models of ancient Greek.
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          Stopponi, Silvia
          Peels-Matthey, Saskia
          Nissim, Malvina
        affil: Centre for Language and Cognition Groningen, University of Groningen , Postbus 716, 9700 AS Groningen, The Netherlands
      su:
        Language models
        Natural language processing
        Native language
        Semantics
      sug:
        subj:
          Language models
          Natural language processing
          Native language
          Semantics
      keyword:
        ancient Greek
        ancient languages
        computational
        digital humanities
        historical language
        linguistics
        natural language processing
        semantics
      ab: The last years have seen the application of Natural Language Processing, in particular, language models, to the study of the Semantics of ancient Greek, but only a little work has been done to create gold data for the evaluation of such models. In this contribution we introduce AGREE, the first benchmark for intrinsic evaluation of semantic models of ancient Greek created from expert judgements. In the absence of native speakers, eliciting expert judgements to create a gold standard is a way to leverage a competence that is the closest to that of natives. Moreover, this method allows for collecting data in a uniform way and giving precise instructions to participants. Human judgements about word relatedness were collected via two questionnaires: in the first, experts provided related lemmas to some proposed seeds, while in the second, they assigned relatedness judgements to pairs of lemmas. AGREE was built from a selection of the collected data.
      pubtype: Academic Journal
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    language: English
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