Paper: Structured Local Training and Biased Potential Functions for Conditional Random Fields with Application to Coreference Resolution

ACL ID N07-1009
Title Structured Local Training and Biased Potential Functions for Conditional Random Fields with Application to Coreference Resolution
Venue Human Language Technologies
Session Main Conference
Year 2007
Authors

Conditional Random Fields (CRFs) have shown great success for problems involving structured out- put variables. However, for many real-world NLP applications, exact maximum-likelihood training is intractable because computing the global normal- ization factor even approximately can be extremely hard. In addition, optimizing likelihood often does not correlate with maximizing task-specific evalu- ation measures. In this paper, we present a novel training procedure, structured local training, that maximizes likelihood while exploiting the benefits of global inference during training: hidden vari- ables are used to capture interactions between lo- cal inference and global inference. Furthermore, we introduce biased potential functions that empir- ically drive CRFs towards performance improve-...