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Recognizing Textual Entailment and Paraphrases in Portuguese

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Progress in Artificial Intelligence (EPIA 2017)

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

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Abstract

The aim of textual entailment and paraphrase recognition is to determine whether the meaning of a text fragment can be inferred (is entailed) from the meaning of another text fragment. In this paper, we address the task of automatically recognizing textual entailment (RTE) and paraphrases from text written in the Portuguese language employing supervised machine learning techniques. Firstly, we formulate the task as a multi-class classification problem. We conclude that semantic-based approaches are very promising to recognize textual entailment and that combining data from European and Brazilian Portuguese brings several challenges typical with cross-language learning. Then, we formulate the task as a binary classification problem and demonstrate the capability of the proposed classifier for RTE and paraphrases. The results reported in this work are promising, achieving 0.83 of accuracy on the test data.

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  1. 1.

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Acknowledgments

The first author is partially supported by a doctoral grant from Doctoral Program in Informatics Engineering (ProDEI) from the Faculty of Engineering of the University of Porto (FEUP).

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Authors and Affiliations

  1. LIACC/DEI, Faculdade de Engenharia, Universidade do Porto, rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Gil Rocha & Henrique Lopes Cardoso

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  1. Gil Rocha
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  2. Henrique Lopes Cardoso
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Correspondence to Gil Rocha .

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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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Rocha, G., Lopes Cardoso, H. (2017). Recognizing Textual Entailment and Paraphrases 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_70

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  • DOI: https://doi.org/10.1007/978-3-319-65340-2_70

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