Paper: Latent Structure Perceptron with Feature Induction for Unrestricted Coreference Resolution

ACL ID W12-4502
Title Latent Structure Perceptron with Feature Induction for Unrestricted Coreference Resolution
Venue International Conference on Computational Natural Language Learning
Session shared task
Year 2012
Authors

We describe a machine learning system based on large margin structure perceptron for unre- stricted coreference resolution that introduces two key modeling techniques: latent corefer- ence trees and entropy guided feature induc- tion. The proposed latent tree modeling turns the learning problem computationally feasi- ble. Additionally, using an automatic feature induction method, we are able to efficiently build nonlinear models and, hence, achieve high performances with a linear learning algo- rithm. Our system is evaluated on the CoNLL- 2012 Shared Task closed track, which com- prises three languages: Arabic, Chinese and English. We apply the same system to all lan- guages, except for minor adaptations on some language dependent features, like static lists of pronouns. Our system achieve...