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.

CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages / Pasini, Tommaso; Scozzafava, Federico; Scarlini, Bianca. - (2020). (Intervento presentato al convegno 58th Annual Meeting of the Association for Computational Linguistics tenutosi a Online).

CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages

Tommaso Pasini
Conceptualization
;
Federico Scozzafava
Software
;
Bianca Scarlini
Writing – Review & Editing
2020

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.
2020
58th Annual Meeting of the Association for Computational Linguistics
Word Sense Disambiguation, Natural Language Processing, Word Sense Distribution
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages / Pasini, Tommaso; Scozzafava, Federico; Scarlini, Bianca. - (2020). (Intervento presentato al convegno 58th Annual Meeting of the Association for Computational Linguistics tenutosi a Online).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1428925
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