Difference between revisions of "Klein et al, CONLL 2003"

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(Created page with '== Citation == Dan Klein, Joseph Smarr, Huy Nguyen and Christopher D. Manning. 2003. Named Entity Recognition with Character-Level Model. In Proceedings of CoNLL-2003. == Onli…')
 
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== Summary ==
 
== Summary ==
This [[Category::paper]] describes a [[UsesMethod::Conditional Random Fields]] approach to the Arabic [[AddressesProblem::Named Entity Recognition]] problem. Arabic is a highly inflectional language in which words can take both prefixes and suffixes. In addition to the complex morphology of Arabic, there is also the absence of capital letters which makes NER task even harder.   
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In this [[Category::paper]], the authors propose using character representations instead of word representations in the [[AddressesProblem::Named Entity Recognition]] task.
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In word model,
  
Previous to this paper, the authors were using Maximum Entropy model ([[RelatedPaper::Benajiba et al, CICLing 2007]])
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[[UsesMethod::Conditional Random Fields]] approach to the Arabic [[AddressesProblem::Named Entity Recognition]] problem. Arabic is a highly inflectional language in which words can take both prefixes and suffixes. In addition to the complex morphology of Arabic, there is also the absence of capital letters which makes NER task even harder.   
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A previous paper that uses character-level approach was the [[RelatedPaper::Cucerzan and Yarowsky, SIGDAT 1999]]. In that paper the authors used the prefix and suffix tries but in this paper all the characters are used.

Revision as of 22:54, 30 November 2010

Citation

Dan Klein, Joseph Smarr, Huy Nguyen and Christopher D. Manning. 2003. Named Entity Recognition with Character-Level Model. In Proceedings of CoNLL-2003.

Online version

ACL Anthology

Summary

In this paper, the authors propose using character representations instead of word representations in the Named Entity Recognition task. In word model,

Conditional Random Fields approach to the Arabic Named Entity Recognition problem. Arabic is a highly inflectional language in which words can take both prefixes and suffixes. In addition to the complex morphology of Arabic, there is also the absence of capital letters which makes NER task even harder.    

A previous paper that uses character-level approach was the Cucerzan and Yarowsky, SIGDAT 1999. In that paper the authors used the prefix and suffix tries but in this paper all the characters are used.