Abstract: Coreference resolution is one of the first stages in deep language understanding and its importance has been well recognized in the natural language processing community. In this paper, we propose a generative, unsupervised ranking model for entity coreference resolution by introducing resolution mode variables. Our unsupervised system achieves 58.44% F1 score of the CoNLL metric on the English data from the CoNLL-2012 shared task (Pradhan et al., 2012), outperforming the Stanford deterministic system (Lee et al., 2013) by 3.01%.

Introduction: Entity coreference resolution has become a critical component for many Natural Language Processing (NLP) tasks. Systems requiring deep language understanding, such as information extraction BIBREF2 , semantic event learning BIBREF3 , BIBREF4 , and named entity linking BIBREF5 , BIBREF6 all benefit from entity coreference information. Entity coreference resolution is the task of identifying mentions (i.e., noun phrases) in a text or dialogue that refer to the same real-world entities. In recent years, several supervised entity coreference resolution systems have been proposed, which, according to ng:2010:ACL, can be categorized into three classes — mention-pair models BIBREF7 , entity-mention models BIBREF8 , BIBREF9 , BIBREF10 and ranking models BIBREF11 , BIBREF12 , BIBREF13 — among which ranking models recently obtained state-of-the-art performance. However, the manually annotated corpora that these systems rely on are highly expensive to create, in particular when we want to build data for resource-poor languages BIBREF14 . That makes unsupervised approaches, which only require unannotated text for training, a desirable solution to this problem. Several unsupervised learning algorithms have been applied to coreference resolution. haghighi-klein:2007:ACLMain presented a mention-pair nonparametric fully-generative Bayesian model for unsupervised coreference resolution. Based on this model, ng:2008:EMNLP probabilistically induced coreference partitions via EM clustering. poon-domingos:2008:EMNLP proposed an entity-mention model that is able to perform joint inference across mentions by using Markov Logic. Unfortunately, these unsupervised systems' performance on accuracy significantly falls behind those of supervised systems, and are even worse than the deterministic rule-based systems. Furthermore, there is no previous work exploring the possibility of developing an unsupervised ranking model which achieved state-of-the-art performance under supervised settings for entity coreference resolution. In this paper, we propose an unsupervised generative ranking model for entity coreference resolution. Our experimental results on the English data from the CoNLL-2012 shared task BIBREF0 show that our unsupervised system outperforms the Stanford deterministic system BIBREF1 by 3.01% absolute on the CoNLL official metric. The contributions of this work are (i) proposing the first unsupervised ranking model for entity coreference resolution. (ii) giving empirical evaluations of this model on benchmark data sets. (iii) considerably narrowing the gap to supervised coreference resolution accuracy.

Question: Is the model presented in the paper state of the art?
Answer: No, supervised models perform better for this task.