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Unsupervised Approaches for Computing Word Similarity in Portuguese

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Book cover Progress in Artificial Intelligence (EPIA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10423))

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Abstract

This paper presents several approaches for computing word similarity in Portuguese and is motivated by the recent availability of state-of-the-art distributional models of Portuguese words, which add to several lexical knowledge bases (LKBs) for this language, available for a longer time. The previous resources were exploited to answer word similarity tests, also recently available for Portuguese. We conclude that there are several valid approaches for this task, but not one that outperforms all the others in every single test. For instance, distributional models seem to capture relatedness better, but LKBs are better suited for computing genuine similarity.

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Notes

  1. 1.

    https://www.aclweb.org/aclwiki/index.php?title=Similarity_(State_of_the_art).

  2. 2.

    http://www.inf.pucrs.br/linatural/wikimodels/similarity.html.

  3. 3.

    http://metashare.metanet4u.eu/.

  4. 4.

    RG-65 also targets similarity but, as far as we know, the process of differentiating similarity and relatedness was much more thorough in SimLex-999.

  5. 5.

    https://github.com/nlx-group/lx-dsemvectors/.

  6. 6.

    https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md.

  7. 7.

    http://paginas.fe.up.pt/~arocha/AED1/0607/trabalhos/thesaurus.txt.

  8. 8.

    http://pt.wiktionary.org (2015 dump).

  9. 9.

    http://ontopt.dei.uc.pt/.

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Authors and Affiliations

  1. CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Hugo Gonçalo Oliveira

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  1. Hugo Gonçalo Oliveira
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Correspondence to Hugo Gonçalo Oliveira .

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Editors and Affiliations

  1. Universidade do Porto, Porto, Portugal

    Eugénio Oliveira

  2. Universidade do Porto, Porto, Portugal

    João Gama

  3. Polytechnic Institute of Porto, Porto, Portugal

    Zita Vale

  4. Universidade do Porto, Porto, Portugal

    Henrique Lopes Cardoso

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Gonçalo Oliveira, H. (2017). Unsupervised Approaches for Computing Word Similarity in Portuguese. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_67

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  • DOI: https://doi.org/10.1007/978-3-319-65340-2_67

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