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        <title>CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages</title>
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    <name type="personal">
        <namePart type="given">Tommaso</namePart>
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    <abstract>Knowing the Most Frequent Sense (MFS) of a word has been proved to help Word Sense Disambiguation (WSD) models significantly. However, the scarcity of sense-annotated data makes it difficult to induce a reliable and high-coverage distribution of the meanings in a language vocabulary. To address this issue, in this paper we present CluBERT, an automatic and multilingual approach for inducing the distributions of word senses from a corpus of raw sentences. Our experiments show that CluBERT learns distributions over English senses that are of higher quality than those extracted by alternative approaches. When used to induce the MFS of a lemma, CluBERT attains state-of-the-art results on the English Word Sense Disambiguation tasks and helps to improve the disambiguation performance of two off-the-shelf WSD models. Moreover, our distributions also prove to be effective in other languages, beating all their alternatives for computing the MFS on the multilingual WSD tasks. We release our sense distributions in five different languages at https://github.com/SapienzaNLP/clubert.</abstract>
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%0 Conference Proceedings
%T CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages
%A Pasini, Tommaso
%A Scozzafava, Federico
%A Scarlini, Bianca
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 jul
%I Association for Computational Linguistics
%C Online
%F pasini-etal-2020-clubert
%X Knowing the Most Frequent Sense (MFS) of a word has been proved to help Word Sense Disambiguation (WSD) models significantly. However, the scarcity of sense-annotated data makes it difficult to induce a reliable and high-coverage distribution of the meanings in a language vocabulary. To address this issue, in this paper we present CluBERT, an automatic and multilingual approach for inducing the distributions of word senses from a corpus of raw sentences. Our experiments show that CluBERT learns distributions over English senses that are of higher quality than those extracted by alternative approaches. When used to induce the MFS of a lemma, CluBERT attains state-of-the-art results on the English Word Sense Disambiguation tasks and helps to improve the disambiguation performance of two off-the-shelf WSD models. Moreover, our distributions also prove to be effective in other languages, beating all their alternatives for computing the MFS on the multilingual WSD tasks. We release our sense distributions in five different languages at https://github.com/SapienzaNLP/clubert.
%R 10.18653/v1/2020.acl-main.369
%U https://aclanthology.org/2020.acl-main.369
%U https://doi.org/10.18653/v1/2020.acl-main.369
%P 4008-4018

Markdown (Informal)

[CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages](https://aclanthology.org/2020.acl-main.369) (Pasini et al., ACL 2020)

• CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages (Pasini et al., ACL 2020)

ACL

• Tommaso Pasini, Federico Scozzafava, and Bianca Scarlini. 2020. CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4008–4018, Online. Association for Computational Linguistics.