An Introduction to Conditional Random Fields

Charles Sutton, Andrew McCallum

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Many tasks involve predicting 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. They combine 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 survey describes conditional random fields, a popular probabilistic method for structured prediction. CRFs have seen wide application in many areas, including 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 survey is intended to be useful to practitioners in a wide variety of fields.
Original languageEnglish
Pages (from-to)267-373
Number of pages109
JournalFoundations and Trends in Machine Learning
Issue number4
Publication statusPublished - 2012


Dive into the research topics of 'An Introduction to Conditional Random Fields'. Together they form a unique fingerprint.

Cite this