FEATURE INDUCTION OF LINEAR-CHAIN CONDITIONAL RANDOM FIELDS A Study Based on a Simulation

Conditional Random Fields (CRFs) is a probabilistic framework for labeling sequential data. Several approaches were developed to automatically induce features for CRFs. They have been successfully applied in real-world applications, e.g. in natural language processing. The work described in this paper was originally motivated by processing the sequence data of table soccer games. As labeling such data is very time consuming, we developed a sequence generator (simulation), which creates an extra phase to explore several basic issues of the feature induction of linear-chain CRFs. First, we generated data sets with different configurations of overlapped and conjunct atomic features, and discussed how these factors affect the induction. Then, a reduction step was integrated into the induction which maintained the prediction accuracy and saved the computational power. Finally, we developed an approach which consists of a queue of CRFs. The experiments show that the CRF queue achieves better results on the data sets in all the configurations.