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Exploring Different Methods for Solving Analogies with Portuguese Word Embeddings

Authors Tiago Sousa, Hugo Gonçalo Oliveira , Ana Alves

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Author Details

Tiago Sousa
  • ISEC, Polytechnic Institute of Coimbra, Portugal
Hugo Gonçalo Oliveira
  • CISUC, Department of Informatics Engineering, University of Coimbra, Portugal
Ana Alves
  • CISUC, University of Coimbra, Portugal
  • ISEC, Polytechnic Institute of Coimbra, Portugal

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Tiago Sousa, Hugo Gonçalo Oliveira, and Ana Alves. Exploring Different Methods for Solving Analogies with Portuguese Word Embeddings. In 9th Symposium on Languages, Applications and Technologies (SLATE 2020). Open Access Series in Informatics (OASIcs), Volume 83, pp. 9:1-9:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/OASIcs.SLATE.2020.9

Abstract

A common way of assessing static word embeddings is to use them for solving analogies of the kind "what is to king as man is to woman?". For this purpose, the vector offset method (king - man + woman = queen), also known as 3CosAdd, has been effectively used for solving analogies and assessing different models of word embeddings in different languages. However, some researchers pointed out that this method is not the most effective for this purpose. Following this, we tested alternative analogy solving methods (3CosMul, 3CosAvg, LRCos) in Portuguese word embeddings and confirmed the previous statement. Specifically, those methods are used to answer the Portuguese version of the Google Analogy Test, dubbed LX-4WAnalogies, which covers syntactic and semantic analogies of different kinds. We discuss the accuracy of different methods applied to different models of embeddings and take some conclusions. Indeed, all methods outperform 3CosAdd, and the best performance is consistently achieved with LRCos, in GloVe.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Lexical semantics
Keywords
  • analogies
  • word embeddings
  • semantic relations
  • syntactic relations
  • Portuguese

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