The knowledge acquisition bottleneck problem dramatically hampers the creation of sense-annotated data for Word Sense Disambiguation (WSD). Sense-annotated data are scarce for English and almost absent for other languages. This limits the range of action of deep-learning approaches, which today are at the base of any NLP task and are hungry for data. We mitigate this issue and encourage further research in multilingual WSD by releasing to the NLP community five large datasets annotated with word-senses in five different languages, namely, English, French, Italian, German and Spanish, and 5 distinct datasets in English, each for a different semantic domain. We show that supervised WSD models trained on our data attain higher performance than when trained on other automatically-created corpora. We release all our data containing more than 15 million annotated instances in 5 different languages at http://trainomatic.org/onesec.

Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains / Scarlini, Bianca; Pasini, Tommaso; Navigli, Roberto. - (2020), pp. 5905-5911. (Intervento presentato al convegno 12th language resources and evaluation conference tenutosi a Marseille; France).

Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains

Scarlini Bianca
Primo
;
Pasini Tommaso
Secondo
;
Navigli Roberto
Ultimo
2020

Abstract

The knowledge acquisition bottleneck problem dramatically hampers the creation of sense-annotated data for Word Sense Disambiguation (WSD). Sense-annotated data are scarce for English and almost absent for other languages. This limits the range of action of deep-learning approaches, which today are at the base of any NLP task and are hungry for data. We mitigate this issue and encourage further research in multilingual WSD by releasing to the NLP community five large datasets annotated with word-senses in five different languages, namely, English, French, Italian, German and Spanish, and 5 distinct datasets in English, each for a different semantic domain. We show that supervised WSD models trained on our data attain higher performance than when trained on other automatically-created corpora. We release all our data containing more than 15 million annotated instances in 5 different languages at http://trainomatic.org/onesec.
2020
12th language resources and evaluation conference
word sense disambiguation; semantics; multilinguality
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains / Scarlini, Bianca; Pasini, Tommaso; Navigli, Roberto. - (2020), pp. 5905-5911. (Intervento presentato al convegno 12th language resources and evaluation conference tenutosi a Marseille; France).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1431886
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