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Semantic role labeling for Arabic language using case-based reasoning approach

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Abstract

Many natural language processing areas use semantic roles in order to improve the applications of the extracted information, the question answering and the machine translation, etc. In Arabic, the work of constructing the semantic role labeling system or the annotated corpus is extremely limited compared to their speaker’s number and to English language as well. In this paper, we present a supervised method for the semantic role labeling of Arabic sentences. Hence, we use the feedback capacity of the case-based reasoning to annotate new sentences from already annotated ones besides the use of the Arabic PropBank as a reference to the semantic labels. We test our method under a wide range corpus that contains 2332 attributes and 5291 arguments. Accordingly, an Arabic semantic role labeling system is tested, for the first time, in that corpus. As a result, our method shows the ability to annotate new sentences from the labeled sentences or the construction of the annotated corpus.

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Notes

  1. http://conll.cemantix.org/2012/introduction.html.

  2. https://catalog.ldc.upenn.edu/LDC2013T19.

  3. http://nlp.cs.swarthmore.edu/semeval/.

  4. http://conll.cemantix.org/2012/data.html.

  5. https://www.ldc.upenn.edu/.

  6. https://catalog.ldc.upenn.edu/LDC2013T19.

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Authors and Affiliations

  1. Department of Computer Science, Badji Mokhtar – Annaba University, P.O. Box 12, 23000, Annaba, Algeria

    Hamza Meguehout

  2. LRI Laboratory, Department of Computer Science, Badji Mokhtar – Annaba University, P.O. Box 12, 23000, Annaba, Algeria

    Tahar Bouhadada & Mohamed Tayeb Laskri

Authors
  1. Hamza Meguehout
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  2. Tahar Bouhadada
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  3. Mohamed Tayeb Laskri
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Correspondence to Hamza Meguehout.

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Meguehout, H., Bouhadada, T. & Laskri, M.T. Semantic role labeling for Arabic language using case-based reasoning approach. Int J Speech Technol 20, 363–372 (2017). https://doi.org/10.1007/s10772-017-9412-6

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