Abstract: With the rise and development of deep learning,more and more researchers begin to apply deep learning technology to coreference resolution.However,existing neural coreference resolution models only focus on the sequential information of text and ignore the integration of structural information which has been proved to be very useful in traditional methods.Based on the neural coreference model proposed by Lee et al.,which has the best performance at present,two measures to solve the problem mentioned above with the help of the constituency parse tree were proposed.Firstly,node enumeration was used to replace the original span extraction strategy.It avoids the restriction of span length and reduces the number of spans that don’t satisfy syntactic rules.Secondly,node sequences are obtained through tree traversal,and the features such as height and path are combined to generate the context representation of the constituency parse trees directly.It avoids the problem of missing structural information caused by the use of word and character sequences only.A lot of experiments were conducted on the dataset of CoNLL 2012 Shared Task,and the proposed model achieves 62.35 average F1 for Chinese and 67.24 average F1 for English,which show that the proposed structural information integration strategy can improve the performance of coreference resolution significantly.

Key words: Constituency parse tree, Coreference resolution, Embedding, Height features, Structural information