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An Introduction to Conditional Random Fields

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(2010)cite arxiv:1011.4088Comment: 90 pages.

Abstract

Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination of classification and graphical modeling, combining the ability of graphical models to compactly model multivariate data with the ability of classification methods to perform prediction using large sets of input features. This tutorial describes conditional random fields, a popular probabilistic method for structured prediction. CRFs have seen wide application in natural language processing, computer vision, and bioinformatics. We describe methods for inference and parameter estimation for CRFs, including practical issues for implementing large scale CRFs. We do not assume previous knowledge of graphical modeling, so this tutorial is intended to be useful to practitioners in a wide variety of fields.

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Comments and Reviewsshow / hide

  • anonymous
    13 years ago
    Nice intro, well written
  • @clemensbaier
    13 years ago
  • @hotho
    13 years ago
    very good introduction into the field of crf and sequence labeling...
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