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Evaluation and Enrichment of Stanford Parser Using an Arabic Property Grammar

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10761))

Abstract

So far, the Stanford Arabic statistic parser is considered as the best parsing tool in terms of performance compared to other parsers. This performance is not stable and may vary depending on the given corpus. A more detailed method to evaluate this parser may help the users to address the causes of a performance loss. We propose, for this reason, to evaluate the Stanford Parser using the verification of the satisfaction of the syntactic constraints (called, properties) based on the analysis results of the corpus. We may obtain these properties from a reference Arabic property grammar. By the way, we enriched the simple representation of the parsing result with syntactic properties. This allows to explicit several implicit information that are the relations between syntactic units. Therefore, we had both a detailed method for the evaluation of parsers and a more syntactically informative representation for the analysis. We obtained widely detailed and encouraging results.

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Notes

  1. 1.

    http://nlp.cs.nyu.edu/evalb/ (of Sekine, S. and Collins, M. in 2006)

  2. 2.

    http://www.informatics.susx.ac.uk/re-search/nlp/carroll/greval.html (of Carroll, J. in 2006).

  3. 3.

    From the Arabic book “زهرة بابنج للعصفورة” (chamomile flower to the bird) of Talal Hassan: http://www.awu-dam.org/book/02/child02/105-t-h/105-t-h.zip.

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Correspondence to Kais Haddar .

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Bahloul, R.B., Kadri, N., Haddar, K., Blache, P. (2018). Evaluation and Enrichment of Stanford Parser Using an Arabic Property Grammar. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_14

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  • DOI: https://doi.org/10.1007/978-3-319-77113-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77112-0

  • Online ISBN: 978-3-319-77113-7

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