2003 NamedEntityRecognitionwithChara

• (Klein et al., 2003) ⇒ Dan Klein, Joseph Smarr, Huy Nguyen, and Christopher D. Manning. (2003). “Named Entity Recognition with Character-level Models.” In: Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4. doi:10.3115/1119176.1119204

Subject Headings: Supervised NER Algorithm, Text Item Predictor Feature, Word-Internal Substring Feature, Character n-Gram Feature.

Cited By

• http://scholar.google.com/scholar?q=%22Named+entity+recognition+with+character-level+models%22+2003
• http://dl.acm.org/citation.cfm?id=1119176.1119204&preflayout=flat#citedby

Quotes

Abstract

We discuss two named-entity recognition models which use characters and character n-grams either exclusively or as an important part of their data representation. The first model is a character-level HMM with minimal context information, and the second model is a maximum-entropy conditional markov model with substantially richer context features. Our best model achieves an overall [math]\displaystyle{ F_1 }[/math] of 86.07% on the English test data (92.31% on the development data). This number represents a 25% error reduction over the same model without word-internal (substring) features.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2003 NamedEntityRecognitionwithChara	Dan Klein Christopher D. Manning Huy Nguyen Joseph Smarr			Named Entity Recognition with Character-level Models				10.3115/1119176.1119204