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        <title>Just “OneSeC” for Producing Multilingual Sense-Annotated Data</title>
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    <abstract>The well-known problem of knowledge acquisition is one of the biggest issues in Word Sense Disambiguation (WSD), where annotated data are still scarce in English and almost absent in other languages. In this paper we formulate the assumption of One Sense per Wikipedia Category and present OneSeC, a language-independent method for the automatic extraction of hundreds of thousands of sentences in which a target word is tagged with its meaning. Our automatically-generated data consistently lead a supervised WSD model to state-of-the-art performance when compared with other automatic and semi-automatic methods. Moreover, our approach outperforms its competitors on multilingual and domain-specific settings, where it beats the existing state of the art on all languages and most domains. All the training data are available for research purposes at http://trainomatic.org/onesec.</abstract>
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%0 Conference Proceedings
%T Just “OneSeC” for Producing Multilingual Sense-Annotated Data
%A Scarlini, Bianca
%A Pasini, Tommaso
%A Navigli, Roberto
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F scarlini-etal-2019-just
%X The well-known problem of knowledge acquisition is one of the biggest issues in Word Sense Disambiguation (WSD), where annotated data are still scarce in English and almost absent in other languages. In this paper we formulate the assumption of One Sense per Wikipedia Category and present OneSeC, a language-independent method for the automatic extraction of hundreds of thousands of sentences in which a target word is tagged with its meaning. Our automatically-generated data consistently lead a supervised WSD model to state-of-the-art performance when compared with other automatic and semi-automatic methods. Moreover, our approach outperforms its competitors on multilingual and domain-specific settings, where it beats the existing state of the art on all languages and most domains. All the training data are available for research purposes at http://trainomatic.org/onesec.
%R 10.18653/v1/P19-1069
%U https://aclanthology.org/P19-1069
%U https://doi.org/10.18653/v1/P19-1069
%P 699-709

Markdown (Informal)

[Just “OneSeC” for Producing Multilingual Sense-Annotated Data](https://aclanthology.org/P19-1069) (Scarlini et al., ACL 2019)

• Just “OneSeC” for Producing Multilingual Sense-Annotated Data (Scarlini et al., ACL 2019)

ACL

• Bianca Scarlini, Tommaso Pasini, and Roberto Navigli. 2019. Just “OneSeC” for Producing Multilingual Sense-Annotated Data. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 699–709, Florence, Italy. Association for Computational Linguistics.