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Combining Evidence in Cognate Identification

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Book cover Advances in Artificial Intelligence (Canadian AI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3060))

Abstract

Cognates are words of the same origin that belong to distinct languages. The problem of automatic identification of cognates arises in language reconstruction and bitext-related tasks. The evidence of cognation may come from various information sources, such as phonetic similarity, semantic similarity, and recurrent sound correspondences. I discuss ways of defining the measures of the various types of similarity and propose a method of combining then into an integrated cognate identification program. The new method requires no manual parameter tuning and performs well when tested on the Indoeuropean and Algonquian lexical data.

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Author information

Authors and Affiliations

  1. Department of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8, Canada

    Grzegorz Kondrak

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  1. Grzegorz Kondrak
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Editor information

Editors and Affiliations

  1. University of Windsor, 401 Sunset Avenue, N9B 3P4, Windsor, Ontario, Canada

    Ahmed Y. Tawfik

  2. School of Computer Science, University of Windsor, Windsor, Ontario,  

    Scott D. Goodwin

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© 2004 Springer-Verlag Berlin Heidelberg

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Kondrak, G. (2004). Combining Evidence in Cognate Identification. In: Tawfik, A.Y., Goodwin, S.D. (eds) Advances in Artificial Intelligence. Canadian AI 2004. Lecture Notes in Computer Science(), vol 3060. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24840-8_4

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  • DOI: https://doi.org/10.1007/978-3-540-24840-8_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22004-6

  • Online ISBN: 978-3-540-24840-8

  • eBook Packages: Springer Book Archive

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