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Using Recurrent Neural Networks for Semantic Role Labeling in Portuguese

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Progress in Artificial Intelligence (EPIA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11805))

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Abstract

Semantic Role Labeling is the task of automatically detecting the semantic role played by words or phrases in a sentence. There is a small number of studies dedicated to Semantic Role Labeling in the Portuguese language, and the obtained performance is far from that of the English language. In this article, we propose an end-to-end semantic role labeler for the Portuguese language, which leans on a deep bidirectional long short-term memory neural network architecture. The predictions are used as inputs to an inference stage that employs a global recursive neural parsing algorithm, tailored for the task. We also provide a detailed analysis of the effects of word embedding dimensionality and network depth on the overall performance of the proposed model. The proposed approach outperforms the state-of-the-art approach on the PropBank-Br corpus, while reducing the relative error in approximately 8.74%.

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Notes

  1. 1.

    Available at http://nilc.icmc.usp.br/nlpnet/.

  2. 2.

    The source code is available at https://github.com/dfalci/deep_pt_srl.

  3. 3.

    Sequences of numbers were transformed into the ‘#’ token while email addresses and URLs were replaced by the ‘@’ token.

  4. 4.

    The training time varies according to the dimensionality.

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Acknowledgment

This work was partially funded by ANEEL Brazil R&D Project CEMIG GT641.

Author information

Authors and Affiliations

  1. Faculty of Business Sciences, Laboratory for Advanced Information Systems - LAIS, FUMEC University, Belo Horizonte, MG, 30310-190, Brazil

    Daniel Henrique Mourão Falci, Marco Antônio Calijorne Soares & Fernando Silva Parreiras

  2. Liaise, P7 Criativo, Belo Horizonte, MG, 30310-190, Brazil

    Daniel Henrique Mourão Falci, Marco Antônio Calijorne Soares, Wladmir Cardoso Brandão & Fernando Silva Parreiras

  3. IRI Research Group, PUC Minas, Belo Horizonte, MG, 30310-190, Brazil

    Wladmir Cardoso Brandão

Authors
  1. Daniel Henrique Mourão Falci
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  2. Marco Antônio Calijorne Soares
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  3. Wladmir Cardoso Brandão
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  4. Fernando Silva Parreiras
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Corresponding author

Correspondence to Fernando Silva Parreiras .

Editor information

Editors and Affiliations

  1. INESC-TEC, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    Paulo Moura Oliveira

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. LIACC/UP, University of Porto, Porto, Portugal

    Luís Paulo Reis

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Falci, D.H.M., Soares, M.A.C., Brandão, W.C., Parreiras, F.S. (2019). Using Recurrent Neural Networks for Semantic Role Labeling in Portuguese. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11805. Springer, Cham. https://doi.org/10.1007/978-3-030-30244-3_56

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  • DOI: https://doi.org/10.1007/978-3-030-30244-3_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30243-6

  • Online ISBN: 978-3-030-30244-3

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