Summary of the paper

Title Improving NER in Arabic Using a Morphological Tagger
Authors Benjamin Farber, Dayne Freitag, Nizar Habash and 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.
Language Single language
Topics Named Entity recognition, Morphology, Lexicon, lexical database
Full paper Improving NER in Arabic Using a Morphological Tagger
Slides Improving NER in Arabic Using a Morphological Tagger
Bibtex @InProceedings{FARBER08.625,
  author = {Benjamin Farber, Dayne Freitag, Nizar Habash and Owen Rambow},
  title = {Improving NER in Arabic Using a Morphological Tagger},
  booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
  year = {2008},
  month = {may},
  date = {28-30},
  address = {Marrakech, Morocco},
  editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-4-0},
  note = {http://www.lrec-conf.org/proceedings/lrec2008/},
  language = {english}
  }

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