Determining Differences of Granularity between Cross-Dictionary Linked Senses

descriptionPublicationkeyboard_double_arrow_right Conference object , Article 01 Jan 2020 English Publisher:ZenodoJournal:Proceedings of EURALEX 2020Funded by:EC | Pret-a-LLOD

Authors: Kouvara, Eirini; Gonzàlez, Meritxell; Grosse, Julian; Saurí, Roser;

doi: 10.5281/zenodo.3952208 , 10.5281/zenodo.3952207

Determining Differences of Granularity between Cross-Dictionary Linked Senses

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Abstract

We present a machine learning model classifying the relation between two linked dictionary senses based on the senses’ granularity. The relations are perfect, narrower/wider, and partial. The model yields an overall accuracy of 86% and significantly outperforms a rule-based algorithm serving as the baseline.We present a machine learning model classifying the relation between two linked dictionary senses based on the senses’ granularity. The relations are perfect, narrower/wider, and partial. The model yields an overall accuracy of 86% and significantly outperforms a rule-based algorithm serving as the baseline.

Related Organizations

Oxford University Press (United Kingdom)
United Kingdom
Oxford University Press
United Kingdom

Keywords

sense granularity, language data generation, word sense mapping, multilingual data, word sense linking, lexical resources, data integration across languages

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