Paper Info
Title
Latent trees for coreference resolution
Abstract
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 datasets, 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.
Year
DOI
Venue
2014
10.1162/COLI_a_00200
Computational Linguistics
Field
DocType
Volume
Pronoun,Coreference,Nonlinear system,Arabic,Computer science,Linear model,Structure learning,Natural language processing,Artificial intelligence,Tree modeling,Machine learning
Journal
40
Issue
ISSN
Citations 
4
0891-2017
9
PageRank 
References 
Authors
0.61
59
3
Name
Order
Citations
PageRank
Eraldo R. Fernandes1766.09
Cícero Nogueira dos Santos277137.83
Ruy Luiz Milidiú319220.18