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Construction Grammar Based Annotation Framework for Parsing Tamil

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Computational Linguistics and Intelligent Text Processing (CICLing 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9623))

Abstract

Syntactic parsing in NLP is the task of working out the grammatical structure of sentences. Some of the purely formal approaches to parsing such as phrase structure grammar, dependency grammar have been successfully employed for a variety of languages. While phrase structure based constituent analysis is possible for fixed order languages such as English, dependency analysis between the grammatical units have been suitable for many free word order languages. These approaches rely on identifying the linguistic units based on their formal syntactic properties and establishing the relationships between such units in the form of a tree. Instead, we characterize every morphosyntactic unit as a mapping between form and function on the lines of Construction Grammar and parsing as identification of dependency relations between such conceptual units. Our approach to parser annotation shows an average MALT LAS score of 82.21% on Tamil gold annotated corpus of 935 sentences in a five-fold validation experiment.

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Notes

  1. 1.

    Hindi, Telugu and Bangla accuracies reported are experimented with Computational Paninian Grammar Framework [7]. The Tamil accuracy reported is based on the Universal Treebank results reported by Straka et al. [15].

  2. 2.

    Indian Language Machine Translation Project funded by DIT, Government of India.

  3. 3.

    The gold annotation was carried out by AU-KBC Research Centre, Chennai.

  4. 4.

    http://www.maltparser.org/download.html.

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Authors and Affiliations

  1. International Institute of Information Technology, Gachibowli, Hyderabad, 500032, Telengana, India

    Vigneshwaran Muralidaran & Dipti Misra Sharma

Authors
  1. Vigneshwaran Muralidaran
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  2. Dipti Misra Sharma
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Correspondence to Vigneshwaran Muralidaran .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Muralidaran, V., Misra Sharma, D. (2018). Construction Grammar Based Annotation Framework for Parsing Tamil. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9623. Springer, Cham. https://doi.org/10.1007/978-3-319-75477-2_27

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  • DOI: https://doi.org/10.1007/978-3-319-75477-2_27

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