A Deep Learning Framework for Coreference Resolution Based on Convolutional Neural Network

Recently many researches have shown that word embeddings are able to represent information from word related contexts or its nearest neighborhood words, and thus are applied in many NLP tasks successfully. In this paper, we propose convolutional neural network model to extent word embeddings to mention/antecedent representation. These representations are obtained through convoluting neighboring word embeddings and other contextual information for coreference resolution. We evaluate our system on the English portion of the CoNLL 2012 Shared Task dataset and show that the proposed system achieves a competitive performance compared with the state-of-the-art approaches. We also show that our proposed model especially improves the coreference resolution of long spans significantly.