Textual Entailment for Modern Standard Arabic

Maytham Alabbas

Abstract


This paper summarizes the Doctoral Thesis that examines various techniques to recognizing Arabic textual entailment, deciding whether one fragment of text entails another, where there is an exceptional level of structural and lexical ambiguities. As far as we know, the current work is the first study to apply this task for Arabic. For this purpose, we firstly describe a semi-automatic method for constructing a first Arabic textual entailment dataset. Then, we have investigated various system combination techniques for improving tagging and parsing depending on having accurate linguistic analyses. Finally, we have improved the standard tree edit distance (TED) algorithm. This extended version of TED, ETED, calculates the distance between two trees by applying operations on subtrees and single nodes. The current work also uses the artificial bee colony (ABC) algorithm to automatically guess the edit operations cost for both subtrees and single nodes and to decide thresholds. The current findings were encouraging for Arabic and English RTE-2 test sets.


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References


Alabbas, M. (2013). Textual Entailment for Modern Standard Arabic. PhD Thesis, The University of Manchester, Manchester, UK.

Alabbas, M. (2011). ArbTE: Arabic textual entailment. In Proceedings of the 2nd Student Research Workshop associated with RANLP 2011, Hissar, Bulgaria, pp. 48–53.

Alabbas, M. and Ramsay, A. (2012). Combining black-box taggers and parsers for modern standard Arabic. In Proceedings FedCSIS-2012, IEEE, Wrocław, Poland, pp. 19 –26.

Alabbas, M. and Ramsay, A. (2014). Combining strategies for tagging and parsing Arabic. In Proceedings of the EMNLP 2014 Workshop on ANLP 2014, Doha, Qatar, pp. 73–77.

Alabbas, M. and Ramsay, A. (2012). Improved POS-tagging for Arabic by combining diverse taggers. In Proceedings of AIAI, volume 381, Springer Berlin, Greece, pp. 107–116.

Alabbas, M. and Ramsay, A. (2014). Improved Parsing for Arabic by Combining Diverse Dependency Parsers. LTC 2011, Revised Selected Papers, Lecture Notes in Computer Science, Springer, Vol. 8387, pp. 43–54.

Alabbas, M. and Ramsay, A. (2011). Evaluation of combining data- driven dependency parsers for Arabic. In Proceedings of LTC 2011, Poznań, Poland, pp. 546–550.

Alabbas, M. (2013). A dataset for Arabic textual entailment. In Proceedings of the Student Research Workshop associated with RANLP 2013, Hissar, Bulgaria, pp. 7–13.

Alabbas, M. and Ramsay, A. (2013). Natural language inference for Arabic using extended tree edit distance with subtrees. Journal of Artificial Intelligence Research, 48:1-22.

Alabbas, M. and Ramsay, A. (2013). Optimising tree edit distance with subtrees for textual entailment. In Proceedings of RANLP2013, Hissar, Bulgaria, pp. 9–17.

Alabbas, M. and Ramsay, A. (2012). Dependency tree matching with extended tree edit distance with subtrees for textual entailment. In Proceedings of FedCSIS-2012, IEEE, Wrocław, Poland, pp. 11–18.




DOI: https://doi.org/10.31449/inf.v45i4.3588

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