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Semi-supervised Textual Entailment on Indonesian Wikipedia Data

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Computational Linguistics and Intelligent Text Processing (CICLing 2018)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13396))

Abstract

Recognizing Textual Entailment (RTE) is a research in Natural Language Processing that aims to identify whether there is an entailment relation between two texts. Textual Entailment has been studied in a variety of languages, but it is rare for the Indonesian language. The purpose of the work presented in this paper is to conduct the RTE experiment on Indonesian language dataset. Since manual data creation is costly and time consuming, we choose semi-supervised machine learning approach. We apply co-training algorithm to enlarge small amounts of annotated data, called seeds. With this method, the human effort only needed to annotate the seeds. The data resource used is all from Wikipedia and the entailment pairs are extracted from its revision history. Evaluation of 1,857 sentence pairs labelled with entailment information using our approach achieve accuracy 76%.

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Notes

  1. 1.

    https://dumps.wikimedia.org/.

  2. 2.

    https://github.com/attardi/wikiextractor.

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Acknowledgement

We thank to Mirna Adriani for input and comments. This research was partially supported by PITTA UI Grant Contract No. 410/UN2.R3.1/HKP.05.00/2017. The first author was also partially funded by Bukalapak.

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Authors and Affiliations

  1. Faculty of Computer Science, Universitas Indonesia, Depok, 16424, West Java, Indonesia

    Ken Nabila Setya & Rahmad Mahendra

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  1. Ken Nabila Setya
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  2. Rahmad Mahendra
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Correspondence to Rahmad Mahendra .

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  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Setya, K.N., Mahendra, R. (2023). Semi-supervised Textual Entailment on Indonesian Wikipedia Data. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2018. Lecture Notes in Computer Science, vol 13396. Springer, Cham. https://doi.org/10.1007/978-3-031-23793-5_34

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  • DOI: https://doi.org/10.1007/978-3-031-23793-5_34

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