Difference between revisions of "Ristad and Yianilos 1997 Learning String Edit Distance"

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== Summary ==
 
== Summary ==
  
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In this [[Category::paper]] the authors describe a stochastic model ([[UsesMethod::HMM | HMM]]) for learning a [[AddressesProblem::String Edit Distance]] function, as well as an efficient [[UsesMethod::Expectation_Maximization | EM]] variant for learning edit costs. The authors then present a stochastic solution to [[AddressesProblem::String Classification]], in which the classification is based on the similarity of an observed string, <math>y^v</math>, to an underlying prototype, <math>x^t</math>, from a class, <math>w</math>. The paper describes an efficient algorithm for inducing a joint probability model <math>p(w, y^v|L, \phi)</math> from a corpus, and this model is used to classify new strings. Finally, the described techniques are applied to the problem of learning [[AddressesProblem::Word Pronunciation]] in conversational speech.
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In this [[Category::paper]] the authors describe a stochastic [[UsesMethod::HMM | HMM]] model for learning a [[AddressesProblem::String Edit Distance]] function, as well as an efficient [[UsesMethod::Expectation_Maximization | EM]] variant for learning edit costs. The authors then present a stochastic solution to [[AddressesProblem::String Classification]], in which the classification is based on the similarity of an observed string, <math>y^v</math>, to an underlying prototype, <math>x^t</math>, from a class, <math>w</math>. The paper describes an efficient algorithm for inducing a joint probability model <math>p(w, y^v|L, \phi)</math> from a corpus, and this model is used to classify new strings. Finally, the described techniques are applied to the problem of learning [[AddressesProblem::Word Pronunciation]] in conversational speech.

Revision as of 13:24, 22 September 2011

Citation

Ristad, E.S. and Yianilos, P.N. Learning string-edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20: 522--532, 1998.

Online

Summary

In this paper the authors describe a stochastic HMM model for learning a String Edit Distance function, as well as an efficient EM variant for learning edit costs. The authors then present a stochastic solution to String Classification, in which the classification is based on the similarity of an observed string, , to an underlying prototype, , from a class, . The paper describes an efficient algorithm for inducing a joint probability model from a corpus, and this model is used to classify new strings. Finally, the described techniques are applied to the problem of learning Word Pronunciation in conversational speech.

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