TY - JOUR

T1 - Latent trees for coreference resolution

AU - Fernandes, Eraldo Rezende

AU - dos Santos, Cícero Nogueira

AU - Milidiú, Ruy Luiz

PY - 2014/12/19

Y1 - 2014/12/19

N2 - We describe a structure learning system for unrestricted coreference resolution that explores two key modeling techniques: latent coreference trees and automatic entropy-guided feature induction. The latent tree modeling makes the learning problem computationally feasible because it incorporates a meaningful hidden structure. Additionally, using an automatic feature induction method, we can efficiently build enhanced nonlinear models using linear model learning algorithms. We present empirical results that highlight the contribution of each modeling technique used in the proposed system. Empirical evaluation is performed on the multilingual unrestricted coreference CoNLL-2012 Shared Task data sets, which comprise three languages: Arabic, Chinese, and English. We apply the same system to all languages, except for minor adaptations to some language-dependent features such as nested mentions and specific static pronoun lists. A previous version of this system was submitted to the CoNLL-2012 Shared Task closed track, achieving an official score of 58:69, the best among the competitors. The unique enhancement added to the current system version is the inclusion of candidate arcs linking nested mentions for the Chinese language. By including such arcs, the score increases by almost 4.5 points for that language. The current system shows a score of 60:15, which corresponds to a 3:5% error reduction, and is the best performing system for each of the three languages.

AB - We describe a structure learning system for unrestricted coreference resolution that explores two key modeling techniques: latent coreference trees and automatic entropy-guided feature induction. The latent tree modeling makes the learning problem computationally feasible because it incorporates a meaningful hidden structure. Additionally, using an automatic feature induction method, we can efficiently build enhanced nonlinear models using linear model learning algorithms. We present empirical results that highlight the contribution of each modeling technique used in the proposed system. Empirical evaluation is performed on the multilingual unrestricted coreference CoNLL-2012 Shared Task data sets, which comprise three languages: Arabic, Chinese, and English. We apply the same system to all languages, except for minor adaptations to some language-dependent features such as nested mentions and specific static pronoun lists. A previous version of this system was submitted to the CoNLL-2012 Shared Task closed track, achieving an official score of 58:69, the best among the competitors. The unique enhancement added to the current system version is the inclusion of candidate arcs linking nested mentions for the Chinese language. By including such arcs, the score increases by almost 4.5 points for that language. The current system shows a score of 60:15, which corresponds to a 3:5% error reduction, and is the best performing system for each of the three languages.

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=84918531827&partnerID=8YFLogxK

U2 - 10.1162/COLI_a_00200

DO - 10.1162/COLI_a_00200

M3 - Journal articles

AN - SCOPUS:84918531827

VL - 40

SP - 801

EP - 835

JO - Computational Linguistics

JF - Computational Linguistics

SN - 0891-2017

IS - 4

ER -