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.
