Improved POS-Tagging for Arabic by Combining Diverse Taggers

Abstract : A number of POS-taggers for Arabic have been presented in the literature. These taggers are not in general 100% accurate, and any errors in tagging are likely to lead to errors in the next step of natural language processing. The current work shows an investigation of how the best taggers available today can be improved by combining them. Experimental results show that a very simple approach to combining taggers can lead to significant improvements over the best individual tagger.
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Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.107-116, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_12〉
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Maytham Alabbas, Allan Ramsay. Improved POS-Tagging for Arabic by Combining Diverse Taggers. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-381 (Part I), pp.107-116, 2012, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-33409-2_12〉. 〈hal-01521402〉

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