Conditional Random Fields Explained

Conditional Random Fields is a class of discriminative models best suited to prediction tasks where contextual information or state of the neighbors affect the current prediction. CRFs find their applications in named entity recognition, part of speech tagging, gene prediction, noise reduction and object detection problems, to name a few. In this article, I will first introduce the basic math and jargon related to Markov Random Fields which is an abstraction CRF is built upon. I will then introduce and explain a simple Conditional Random Fields model in detail which will show why are they suited well to sequential prediction problems. After that, I will go over the likelihood maximization problem and related derivations in context of that CRF model.