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            <title>Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)</title>
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            <namePart type="family">Markantonatou</namePart>
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            <namePart type="family">Savary</namePart>
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        <name type="personal">
            <namePart type="given">Veronika</namePart>
            <namePart type="family">Vincze</namePart>
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    <abstract>This paper describes an approach for the classification of millions of existing multi-word entities (MWEntities), such as organisation or event names, into thirteen category types, based only on the tokens they contain. In order to classify our very large in-house collection of multilingual MWEntities into an application-oriented set of entity categories, we trained and tested distantly-supervised classifiers in 43 languages based on MWEntities extracted from BabelNet. The best-performing classifier was the multi-class SVM using a TF.IDF-weighted data representation. Interestingly, one unique classifier trained on a mix of all languages consistently performed better than classifiers trained for individual languages, reaching an averaged F1-value of 88.8%. In this paper, we present the training and test data, including a human evaluation of its accuracy, describe the methods used to train the classifiers, and discuss the results.</abstract>
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%0 Conference Proceedings
%T Multi-word Entity Classification in a Highly Multilingual Environment
%A Chesney, Sophie
%A Jacquet, Guillaume
%A Steinberger, Ralf
%A Piskorski, Jakub
%Y Markantonatou, Stella
%Y Ramisch, Carlos
%Y Savary, Agata
%Y Vincze, Veronika
%S Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F chesney-etal-2017-multi
%X This paper describes an approach for the classification of millions of existing multi-word entities (MWEntities), such as organisation or event names, into thirteen category types, based only on the tokens they contain. In order to classify our very large in-house collection of multilingual MWEntities into an application-oriented set of entity categories, we trained and tested distantly-supervised classifiers in 43 languages based on MWEntities extracted from BabelNet. The best-performing classifier was the multi-class SVM using a TF.IDF-weighted data representation. Interestingly, one unique classifier trained on a mix of all languages consistently performed better than classifiers trained for individual languages, reaching an averaged F1-value of 88.8%. In this paper, we present the training and test data, including a human evaluation of its accuracy, describe the methods used to train the classifiers, and discuss the results.
%R 10.18653/v1/W17-1702
%U https://aclanthology.org/W17-1702
%U https://doi.org/10.18653/v1/W17-1702
%P 11-20

Markdown (Informal)

[Multi-word Entity Classification in a Highly Multilingual Environment](https://aclanthology.org/W17-1702) (Chesney et al., MWE 2017)

• Multi-word Entity Classification in a Highly Multilingual Environment (Chesney et al., MWE 2017)

ACL

• Sophie Chesney, Guillaume Jacquet, Ralf Steinberger, and Jakub Piskorski. 2017. Multi-word Entity Classification in a Highly Multilingual Environment. In Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017), pages 11–20, Valencia, Spain. Association for Computational Linguistics.