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Disambiguation of Universal Dependencies Part-of-Speech Tags of Closed Class Words in Portuguese

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Intelligent Systems (BRACIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14197))

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Abstract

This paper explores methods to disambiguate Part-of-Speech (PoS) tags for closed class words in Brazilian Portuguese corpora annotated according to the Universal Dependencies annotation model. We evaluate disambiguation methods of different paradigms, namely a Markov-based method, a widely adopted parsing tool, and a BERT-based language modeling method. We compare their performances with two baselines, and observe a significant increase of more than 10% over the baselines for all proposed methods. We also show that while the BERT-based model outperforms the others reaching for the best case a 98% accuracy predicting the correct PoS tag, the use of the three methods as an Ensemble method offers more stable result according to the smaller variance for the numerical results we performed.

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Notes

  1. 1.

    Similarly to English with the ending -“ly”, in Portuguese it is possible to turn adjectives into adverbs by adding -“mente” at the end. We disconsider those -ly adverbs, as only the primitive adverbs are a closed class.

  2. 2.

    The verbs ser and estar (“to be” in English) are always annotated as AUX, either by being true auxiliary verbs, either by being copula verbs. The verbs “ir”, “haver”, and “ter” (“to go”, “to exist”, and “to have” in English) are sometimes annotated as VERB, sometimes annotated as AUX (as “going to” and “have” + a past participle in English).

  3. 3.

    While adjectives are not a closed class, the adjectives that are ordinal numbers are considered belonging to a closed subset of class ADJ.

  4. 4.

    https://github.com/nilc-nlp/pos-disambiguation.

  5. 5.

    The values stated as average are the macro average of the values of each fold, but since the folds have about the same size, the values for micro and macro average have are practically the same (less than 0.01% difference).

  6. 6.

    For reproducibility purposes, all data (including fold splits) and implementation of all methods are available at https://sites.google.com/icmc.usp.br/poetisa/publications.

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Acknowledgements

This work was carried out at the Center for Artificial Intelligence (C4AI-USP), with support by the São Paulo Research Foundation (FAPESP grant number 2019/07665-4) and by the IBM Corporation. The project was also supported by the Ministry of Science, Technology and Innovation, with resources of Law N. 8.248, of October 23, 1991, within the scope of PPI-SOFTEX, coordinated by Softex and published as Residence in TIC 13, DOU 01245.010222/2022-44.

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Authors and Affiliations

  1. ICMC, University of São Paulo, São Carlos, Brazil

    Lucelene Lopes, Magali S. Duran & Thiago A. S. Pardo

  2. Merrimack College, North Andover, MA, USA

    Paulo Fernandes

  3. CISUC, Universidade de Coimbra, Coimbra, Portugal

    Marcio L. Inacio

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  1. Lucelene Lopes
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  2. Paulo Fernandes
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Correspondence to Lucelene Lopes .

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  1. Federal University of São Carlos, São Carlos, Brazil

    Murilo C. Naldi

  2. Centro Universitario da FEI, São Bernardo do Campo, Brazil

    Reinaldo A. C. Bianchi

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Lopes, L., Fernandes, P., Inacio, M.L., Duran, M.S., Pardo, T.A.S. (2023). Disambiguation of Universal Dependencies Part-of-Speech Tags of Closed Class Words in Portuguese. In: Naldi, M.C., Bianchi, R.A.C. (eds) Intelligent Systems. BRACIS 2023. Lecture Notes in Computer Science(), vol 14197. Springer, Cham. https://doi.org/10.1007/978-3-031-45392-2_16

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