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On the Utility of Word Embeddings for Enriching OpenWordNet-PT

Authors Hugo Gonçalo Oliveira , Fredson Silva de Souza Aguiar , Alexandre Rademaker

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Author Details

Hugo Gonçalo Oliveira
  • CISUC, Department of Informatics Engineering, University of Coimbra, Portugal
Fredson Silva de Souza Aguiar
  • FGV/EMAp, Rio de Janeiro, Brazil
Alexandre Rademaker
  • IBM Research, Rio de Janeiro, Brazil
  • FGV/EMAp, Rio de Janeiro, Brazil

Funding

  • Gonçalo Oliveira, Hugo: Supported by national funds through FCT, within the scope of the project CISUC~(UID/CEC/00326/2020) and by European Social Fund, through the Regional Operational Program Centro 2020.

Acknowledgements

The research described in this paper was partially conducted in the scope of the COST Action CA18209 Nexus Linguarum (European network for Web-centred linguistic data science).

Cite AsGet BibTex

Hugo Gonçalo Oliveira, Fredson Silva de Souza Aguiar, and Alexandre Rademaker. On the Utility of Word Embeddings for Enriching OpenWordNet-PT. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 21:1-21:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.LDK.2021.21

Abstract

The maintenance of wordnets and lexical knwoledge bases typically relies on time-consuming manual effort. In order to minimise this issue, we propose the exploitation of models of distributional semantics, namely word embeddings learned from corpora, in the automatic identification of relation instances missing in a wordnet. Analogy-solving methods are first used for learning a set of relations from analogy tests focused on each relation. Despite their low accuracy, we noted that a portion of the top-given answers are good suggestions of relation instances that could be included in the wordnet. This procedure is applied to the enrichment of OpenWordNet-PT, a public Portuguese wordnet. Relations are learned from data acquired from this resource, and illustrative examples are provided. Results are promising for accelerating the identification of missing relation instances, as we estimate that about 17% of the potential suggestions are good, a proportion that almost doubles if some are automatically invalidated.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Lexical semantics
  • Computing methodologies → Language resources
Keywords
  • word embeddings
  • lexical resources
  • wordnet
  • analogy tests

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