Crf

This document discusses conditional random fields (CRFs) and their use for named entity recognition and event extraction tasks. It notes that CRFs address the label bias problem of hidden Markov models (HMMs) and maximum entropy Markov models (MEMMs) by conditioning on observations and considering many feature types. CRFs model transition probabilities between states and state probabilities conditioned on observations, using edge features between labels as well as vertex features of the observations.