Improving NER in Arabic Using a Morphological Tagger

Benjamin Farber, Dayne Freitag, Nizar Habash, Owen Rambow


Abstract
We discuss a named entity recognition system for Arabic, and show how we incorporated the information provided by MADA, a full morphological tagger which uses a morphological analyzer. Surprisingly, the relevant features used are the capitalization of the English gloss chosen by the tagger, and the fact that an analysis is returned (that a word is not OOV to the morphological analyzer). The use of the tagger also improves over a third system which just uses a morphological analyzer, yielding a 14\% reduction in error over the baseline. We conduct a thorough error analysis to identify sources of success and failure among the variations, and show that by combining the systems in simple ways we can significantly influence the precision-recall trade-off.
Anthology ID:
L08-1054
Volume:
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)
Month:
May
Year:
2008
Address:
Marrakech, Morocco
Venue:
LREC
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Publisher:
European Language Resources Association (ELRA)
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Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2008/pdf/625_paper.pdf
DOI:
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Cite (ACL):
Benjamin Farber, Dayne Freitag, Nizar Habash, and Owen Rambow. 2008. Improving NER in Arabic Using a Morphological Tagger. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).
Cite (Informal):
Improving NER in Arabic Using a Morphological Tagger (Farber et al., LREC 2008)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2008/pdf/625_paper.pdf