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A Sematic Role Labeling Approach in Myanmar Text

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Genetic and Evolutionary Computing (GEC 2015)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 388))

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  • International Conference on Genetic and Evolutionary Computing

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Abstract

There is a generally certainty in the natural language and computational linguistics communities that semantic role labeling (SRL) is an important step toward improving significant applications, e.g. question answering, text summarization and information extraction. We propose a new method for assigning semantic roles on the structured trees of Myanmar sentences using Myanmar Verb Frame (MVF). In this paper, there is not use any machine learning techniques for SRL. It employs with predicate-argument identification algorithm and mapping algorithm to identify semantic roles in Myanmar. These algorithms mainly work on the syntax structure of Myanmar sentences. This system achieves over 70 % success rate in labeling the semantic role of pre-segmented constituents on the datasets.

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Author information

Authors and Affiliations

  1. Natural Language Processing Research Lab, University of Computer Studies, Mandalay, Myanmar

    May Thu Naing & Aye Thida

Authors
  1. May Thu Naing
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  2. Aye Thida
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Corresponding author

Correspondence to May Thu Naing .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Thi Thi Zin

  2. School of Computer Science and Tech..., Harbin Institute of Technology, Shenzhen Graduate School , Shenzhen, China

    Jerry Chun-Wei Lin

  3. College of Information Science and Engg, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

  4. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Pyke Tin

  5. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Mitsuhiro Yokota

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© 2016 Springer International Publishing Switzerland

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Naing, M.T., Thida, A. (2016). A Sematic Role Labeling Approach in Myanmar Text. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. GEC 2015. Advances in Intelligent Systems and Computing, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-319-23207-2_43

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  • DOI: https://doi.org/10.1007/978-3-319-23207-2_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23206-5

  • Online ISBN: 978-3-319-23207-2

  • eBook Packages: EngineeringEngineering (R0)

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