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        <title>Using support vector machines and state-of-the-art algorithms for phonetic alignment to identify cognates in multi-lingual wordlists</title>
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    <abstract>Most current approaches in phylogenetic linguistics require as input multilingual word lists partitioned into sets of etymologically related words (cognates). Cognate identification is so far done manually by experts, which is time consuming and as of yet only available for a small number of well-studied language families. Automatizing this step will greatly expand the empirical scope of phylogenetic methods in linguistics, as raw wordlists (in phonetic transcription) are much easier to obtain than wordlists in which cognate words have been fully identified and annotated, even for under-studied languages. A couple of different methods have been proposed in the past, but they are either disappointing regarding their performance or not applicable to larger datasets. Here we present a new approach that uses support vector machines to unify different state-of-the-art methods for phonetic alignment and cognate detection within a single framework. Training and evaluating these method on a typologically broad collection of gold-standard data shows it to be superior to the existing state of the art.</abstract>
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%0 Conference Proceedings
%T Using support vector machines and state-of-the-art algorithms for phonetic alignment to identify cognates in multi-lingual wordlists
%A Jäger, Gerhard
%A List, Johann-Mattis
%A Sofroniev, Pavel
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F jager-etal-2017-using
%X Most current approaches in phylogenetic linguistics require as input multilingual word lists partitioned into sets of etymologically related words (cognates). Cognate identification is so far done manually by experts, which is time consuming and as of yet only available for a small number of well-studied language families. Automatizing this step will greatly expand the empirical scope of phylogenetic methods in linguistics, as raw wordlists (in phonetic transcription) are much easier to obtain than wordlists in which cognate words have been fully identified and annotated, even for under-studied languages. A couple of different methods have been proposed in the past, but they are either disappointing regarding their performance or not applicable to larger datasets. Here we present a new approach that uses support vector machines to unify different state-of-the-art methods for phonetic alignment and cognate detection within a single framework. Training and evaluating these method on a typologically broad collection of gold-standard data shows it to be superior to the existing state of the art.
%U https://aclanthology.org/E17-1113
%P 1205-1216

Markdown (Informal)

[Using support vector machines and state-of-the-art algorithms for phonetic alignment to identify cognates in multi-lingual wordlists](https://aclanthology.org/E17-1113) (Jäger et al., EACL 2017)

• Using support vector machines and state-of-the-art algorithms for phonetic alignment to identify cognates in multi-lingual wordlists (Jäger et al., EACL 2017)

ACL

• Gerhard Jäger, Johann-Mattis List, and Pavel Sofroniev. 2017. Using support vector machines and state-of-the-art algorithms for phonetic alignment to identify cognates in multi-lingual wordlists. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1205–1216, Valencia, Spain. Association for Computational Linguistics.