【摘要】 事件同指消解是一项具有挑战性的自然语言处理任务,它在事件抽取、问答系统、阅读理解中有着重要的作用。文中提出了一种基于全局和局部信息,并具有全局推理机制的可分解注意力神经网络模型DANGL(Decomposable Attention Neural network based on Global and Local information),用于文档级的事件同指消解。神经网络模型DANGL与过去大部分以概率模型和图模型为基础的传统方法之间存在很大的区别。DANGL首先使用Bi-LSTM和CNN分别获取每个事件句的全局信息和局部信息;然后使用可分解注意力网络获取每个事件句中相对重要的信息;最后使用文档级全局推理模型进一步优化同指链。在TAC-KBP语料库上的实验显示,DANGL使用了少量的特征,且平均性能优于目前最好的基准系统。更多还原

【Abstract】 Event coreference resolution is a challenging NLP task.It plays an import role in event extraction,QA system and reading comprehension.This paper introduced a decomposable attention neural network model DANGL with global inference mechanism based on remote and local information to document-level event coreference resolution.The neural network model DANGL is quite different from most traditional methods based on probabilistic models and graph models in the past.DANGL first uses Bi-LSTM and CNN to capture both the remote information and the local information of each event mention.Then,it applies the decomposable attention network to capture relatively important information in event mention.Finally,it employs a document-level global inference mechanism to further optimize the coreference chains.Experimental results on TAC-KBP show that DANGL uses a few features and outperforms the state-of-the-art baseline.更多还原