TY - CHAP

T1 - Latent structure perceptron with feature induction for unrestricted coreference resolution

AU - Fernandes, Eraldo Rezende

AU - dos Santos, Cícero Nogueira

AU - Milidiú, Ruy Luiz

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=84918552373&partnerID=8YFLogxK

UR - https://dl.acm.org/action/showFmPdf?doi=10.5555%2F2391181

M3 - Article in conference proceedings

AN - SCOPUS:84918552373

SP - 41

EP - 48

BT - Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

PB - Association for Computational Linguistics (ACL)

CY - Stroudsburg

T2 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning - EMNLP-CoNLL 2012

Y2 - 12 July 2012 through 14 July 2012

ER -