An Introduction to Conditional Random Fields

TL;DR

This paper introduces conditional random fields (CRFs), a probabilistic framework for structured prediction that combines graphical models and classification, with applications across NLP, vision, and bioinformatics.

Contribution

It provides a comprehensive tutorial on CRFs, covering inference, parameter estimation, and practical implementation issues for large-scale problems.

Findings

CRFs are effective for structured prediction tasks.

The tutorial covers inference and parameter estimation methods.

Practical issues for large-scale CRF implementation are discussed.

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.