Paper: Coreference Resolution System using Maximum Entropy Classifier

ACL ID W11-1921
Title Coreference Resolution System using Maximum Entropy Classifier
Venue International Conference on Computational Natural Language Learning
Session shared task
Year 2011
Authors

In this paper, we present our supervised learning approach to coreference resolution in ConLL corpus. The system relies on a maximum entropy-based classifier for pairs of mentions, and adopts a rich linguisitical- ly motivated feature set, which mostly has been introduced by Soon et al (2001), and experiment with alternaive resolution proc- ess, preprocessing tools,and classifiers. We optimize the system’s performance for M- UC (Vilain et al, 1995), BCUB (Bagga and Baldwin, 1998) and CEAF (Luo, 2005) .