一种端到端的事件共指消解方法OACSTPCD

The event coreference resolution(ECR)is mainly to determine whether different event mentions refer to the same event.ECR not only effectively alleviates the problem of information redundancy in event extraction tasks,but also provides an effective way for event completion.Although many scholars have conducted extensive research on ECR using deep learning methods and achieved significant achievements,there are still issues in most ECR models,such as insufficient explicit information representation,noise introduced by arguments,and sparse distribution of coreference events.Aiming at the above problems,an end-to-end ECR method using explicit argument information and event chain reconstruc-tion was proposed.First,an event extraction model called OneIE was used to extract event triggers and arguments.Then,a Transformer encoder is used to express the context of the event mentions,and the confidence score was introduced into the argument information coding to mitigate the error transmission.Meanwhile,the information of the argument in the horizontal and vertical directions of the trigger was decomposed by the gat-ing mechanism,and the noise of the argument was filtered by fusing the information of the directions according to the correlation coefficient of the argument and the trigger.Afterwards,the coreference score of the event pairs was calculated by the feed forward network.Finally,to verify the validity of the event mentions,the event chains were reconstructed to correct the deviation of the model caused by the sparse event corefer-ence.In order to verify the effectiveness of our method,the proposed model is trained and tested on the public dataset ACE2005.The experiment-al results showed that our model in end-to-end ECR task is 5.67%and 6.24%higher than the other models in the scores of CoNLL and AVG on average.