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    <titleInfo>
        <title>Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags</title>
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    <name type="personal">
        <namePart type="given">Onur</namePart>
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    <name type="personal">
        <namePart type="given">Suzan</namePart>
        <namePart type="family">Uskudarli</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Pierre</namePart>
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    <abstract>Previous studies have shown that linguistic features of a word such as possession, genitive or other grammatical cases can be employed in word representations of a named entity recognition (NER) tagger to improve the performance for morphologically rich languages. However, these taggers require external morphological disambiguation (MD) tools to function which are hard to obtain or non-existent for many languages. In this work, we propose a model which alleviates the need for such disambiguators by jointly learning NER and MD taggers in languages for which one can provide a list of candidate morphological analyses. We show that this can be done independent of the morphological annotation schemes, which differ among languages. Our experiments employing three different model architectures that join these two tasks show that joint learning improves NER performance. Furthermore, the morphological disambiguator’s performance is shown to be competitive.</abstract>
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        <date>2018-08</date>
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            <start>2082</start>
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%0 Conference Proceedings
%T Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags
%A Güngör, Onur
%A Uskudarli, Suzan
%A Güngör, Tunga
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F gungor-etal-2018-improving
%X Previous studies have shown that linguistic features of a word such as possession, genitive or other grammatical cases can be employed in word representations of a named entity recognition (NER) tagger to improve the performance for morphologically rich languages. However, these taggers require external morphological disambiguation (MD) tools to function which are hard to obtain or non-existent for many languages. In this work, we propose a model which alleviates the need for such disambiguators by jointly learning NER and MD taggers in languages for which one can provide a list of candidate morphological analyses. We show that this can be done independent of the morphological annotation schemes, which differ among languages. Our experiments employing three different model architectures that join these two tasks show that joint learning improves NER performance. Furthermore, the morphological disambiguator’s performance is shown to be competitive.
%U https://aclanthology.org/C18-1177
%P 2082-2092

Markdown (Informal)

[Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags](https://aclanthology.org/C18-1177) (Güngör et al., COLING 2018)

• Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags (Güngör et al., COLING 2018)

ACL

• Onur Güngör, Suzan Uskudarli, and Tunga Güngör. 2018. Improving Named Entity Recognition by Jointly Learning to Disambiguate Morphological Tags. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2082–2092, Santa Fe, New Mexico, USA. Association for Computational Linguistics.