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        <title>Linguistic classification: dealing jointly with irrelevance and inconsistency</title>
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    <name type="personal">
        <namePart type="given">Laura</namePart>
        <namePart type="family">Franzoi</namePart>
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        <namePart type="given">Andrea</namePart>
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    <name type="personal">
        <namePart type="given">Anca</namePart>
        <namePart type="family">Dinu</namePart>
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    <name type="personal">
        <namePart type="given">Liviu</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>In this paper, we present new methods for language classification which put to good use both syntax and fuzzy tools, and are capable of dealing with irrelevant linguistic features (i.e. features which should not contribute to the classification) and even inconsistent features (which do not make sense for specific languages). We introduce a metric distance, based on the generalized Steinhaus transform, which allows one to deal jointly with irrelevance and inconsistency. To evaluate our methods, we test them on a syntactic data set, due to the linguist G. Longobardi and his school. We obtain phylogenetic trees which sometimes outperform the ones obtained by Atkinson and Gray.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_040</identifier>
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%0 Conference Proceedings
%T Linguistic classification: dealing jointly with irrelevance and inconsistency
%A Franzoi, Laura
%A Sgarro, Andrea
%A Dinu, Anca
%A Dinu, Liviu P.
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F franzoi-etal-2019-linguistic
%X In this paper, we present new methods for language classification which put to good use both syntax and fuzzy tools, and are capable of dealing with irrelevant linguistic features (i.e. features which should not contribute to the classification) and even inconsistent features (which do not make sense for specific languages). We introduce a metric distance, based on the generalized Steinhaus transform, which allows one to deal jointly with irrelevance and inconsistency. To evaluate our methods, we test them on a syntactic data set, due to the linguist G. Longobardi and his school. We obtain phylogenetic trees which sometimes outperform the ones obtained by Atkinson and Gray.
%R 10.26615/978-954-452-056-4_040
%U https://aclanthology.org/R19-1040
%U https://doi.org/10.26615/978-954-452-056-4_040
%P 345-352

Markdown (Informal)

[Linguistic classification: dealing jointly with irrelevance and inconsistency](https://aclanthology.org/R19-1040) (Franzoi et al., RANLP 2019)

• Linguistic classification: dealing jointly with irrelevance and inconsistency (Franzoi et al., RANLP 2019)

ACL

• Laura Franzoi, Andrea Sgarro, Anca Dinu, and Liviu P. Dinu. 2019. Linguistic classification: dealing jointly with irrelevance and inconsistency. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 345–352, Varna, Bulgaria. INCOMA Ltd..