Difference between revisions of "Klein et al, CONLL 2003"
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== Summary == | == Summary == | ||
| − | In this [[Category::paper]], the authors propose using character representations instead of word representations in the [[AddressesProblem::Named Entity Recognition]] task. | + | In this [[Category::paper]], the authors propose using character representations instead of word representations in the [[AddressesProblem::Named Entity Recognition]] task. The first model proposed is the character-level HMM with minimal context information and the second model is maximum-entropy conditional markov model with rich context features. |
| − | In | + | |
| + | In character-level [[UsesMethod::HMM]], each character is represented with one state which depends only on the previous state. And each character observation depends on the current state and on the previous n-1 observations. | ||
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| + | In order to prevent characters of a word getting different state labels, they represent each state with a pair(t,k) where t is entity type and k is length of time of being in a state | ||
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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. | 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 22:03, 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
Summary
In this paper, the authors propose using character representations instead of word representations in the Named Entity Recognition task. The first model proposed is the character-level HMM with minimal context information and the second model is maximum-entropy conditional markov model with rich context features.
In character-level HMM, each character is represented with one state which depends only on the previous state. And each character observation depends on the current state and on the previous n-1 observations.
In order to prevent characters of a word getting different state labels, they represent each state with a pair(t,k) where t is entity type and k is length of time of being in a state
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.
