A large and evolving cognate database.

We present CogNet, a large-scale, automatically-built database of sense-tagged cognates—words of common origin and meaning across languages. CogNet is continuously evolving: its current version contains over 8 million cognate pairs over 338 languages and 35 writing systems, with new releases already...

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Publicado en:Language Resources & Evaluation Vol. 56; no. 1; pp. 165 - 190
Autores principales: Batsuren, Khuyagbaatar, Bella, Gábor, Giunchiglia, Fausto
Formato: Artículo
Publicado: Springer Nature Mar2022
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: A large and evolving cognate database.
      aug:
        au:
          Batsuren, Khuyagbaatar
          Bella, Gábor
          Giunchiglia, Fausto
        affil:
          Department of Information and Computer Science, National University of Mongolia, Ikh surguuliin gudamj 1, 14200, Ulaanbaatar, Mongolia
          Department of Information Engineering and Computer Science, University of Trento, via Sommarive 5, 38123, Trento, Italy
          College of Computer Science and Technology, Jilin University, Changchun, China
      su:
        Etymology
        Databases
        Knowledge base
        Data analysis
        Quantitative research
      sug:
        subj:
          Etymology
          Databases
          Knowledge base
          Data analysis
          Quantitative research
      keyword:
        Cognate
        Lexical database
        Lexical semantics
      ab: We present CogNet, a large-scale, automatically-built database of sense-tagged cognates—words of common origin and meaning across languages. CogNet is continuously evolving: its current version contains over 8 million cognate pairs over 338 languages and 35 writing systems, with new releases already in preparation. The paper presents the algorithm and input resources used for its computation, an evaluation of the result, as well as a quantitative analysis of cognate data leading to novel insights on language diversity. Furthermore, as an example on the use of large-scale cross-lingual knowledge bases for improving the quality of multilingual applications, we present a case study on the use of CogNet for bilingual lexicon induction in the framework of cross-lingual transfer learning.
      pubtype: Academic Journal
      doctype: Article
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    language: English
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