An unsupervised approach based on ART network for coreference resolution of Chinese

This paper proposes a novel unsupervised approach for coreference resolution of Chinese based on adaptive resonance theory (ART) Networks. Through making full use of the characteristics of noun phrases and dynamically adjusting the parameters of the networks, the approach can solve the problem in the present clustering coreference resolution that the number of the output categories is hard to determine. Additionally, the approach performs a feature selection process based on the gain ratio criterion to reduce the noise created by the weak features in differentiation. The method scarcely depends on the hand-labeled corpus and can be directly applied to real texts in multiple fields while ensuring the accuracy. The experiment has shown its encouraging performance on ACE Chinese corpus.