We describe a machine learning system based on large margin structure perceptron for unrestricted coreference resolution that introduces two key modeling techniques: latent coreference trees and entropy guided feature induction. The proposed latent tree modeling turns the learning problem computationally feasible. Additionally, using an automatic feature induction method, we are able to efficiently build nonlinear models and, hence, achieve high performances with a linear learning algorithm. Our system is evaluated on the CoNLL-2012 Shared Task closed track, which comprises three languages: Arabic, Chinese and English. We apply the same system to all languages, except for minor adaptations on some language dependent features, like static lists of pronouns. Our system achieves an official score of 58.69, the best one among all the competitors.
In 2012, at CoNLL, REF presented the following strategy: a machine learning system based on a perceptron algorithm.
Latent Structure Perceptron with Feature Induction for Unrestricted Coreference Resolution
Joint Conference on EMNLP and CoNLL - Shared Task
Computer Science