Abstract: Coreference resolution plays an important role in natural language processing. A supervised correlation clustering algorithm for coreference resolution was proposed. Firstly, coreference resolution was treated as a graph correlalion clustering problem,which partition the coreference relation from the global view,rather to make pairwise coreference decisions independently of each other. Then, the inference algorithms for correlation clustering were presented. Finally, a learning algorithm based on gradient descent was proposed to make the features parameters be trained from the training corpus, so that the learned parameters can better fit the objective of the correlation clustering. The experimental results on the ACE Chinese corpus demonstrate that the proposed method achieves better performance, compared with the traditional approaches.

Key words: Coreference resolution,Correlation clustering,Loss function