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An Empirical Evaluation of AMR Parsing for Legal Documents

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New Frontiers in Artificial Intelligence (JSAI-isAI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11717))

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Abstract

Many approaches have been proposed to tackle the problem of Abstract Meaning Representation (AMR) parsing, help solving various natural language processing issues recently. In our paper, we provide an overview of different methods in AMR parsing and their performances when analyzing legal documents. We conduct experiments of different AMR parsers on our annotated dataset extracted from the English version of Japanese Civil Code. Our results show the limitations as well as open a room for improvements of current parsing techniques when applying in this non-trivial domain.

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Notes

  1. 1.

    https://catalog.ldc.upenn.edu/ldc2017t10.

  2. 2.

    We keep the original trained models without retrained on the new dataset LDC2017T10.

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Acknowledgment

This work was supported by JST CREST Grant Number JPMJCR1513, Japan.

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Authors and Affiliations

  1. Japan Advanced Institute of Science and Technology (JAIST), Nomi, Japan

    Trong Sinh Vu & Le Minh Nguyen

Authors
  1. Trong Sinh Vu
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  2. Le Minh Nguyen
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Corresponding authors

Correspondence to Trong Sinh Vu or Le Minh Nguyen .

Editor information

Editors and Affiliations

  1. National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan

    Kazuhiro Kojima

  2. University of Electro-Communications, Tokyo, Japan

    Maki Sakamoto

  3. Ochanomizu University, Tokyo, Japan

    Koji Mineshima

  4. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

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Vu, T.S., Nguyen, L.M. (2019). An Empirical Evaluation of AMR Parsing for Legal Documents. In: Kojima, K., Sakamoto, M., Mineshima, K., Satoh, K. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2018. Lecture Notes in Computer Science(), vol 11717. Springer, Cham. https://doi.org/10.1007/978-3-030-31605-1_11

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  • DOI: https://doi.org/10.1007/978-3-030-31605-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31604-4

  • Online ISBN: 978-3-030-31605-1

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