摘要:事件抽取旨在从非结构化的文本中提取人们感兴趣的信息，并以结构化的形式呈现给用户.当前，大多数中文事件抽取系统采用连续的管道模型，即：先识别事件触发词，后识别事件元素.其容易产生级联错误，且处于下游的任务无法将信息反馈至上游任务，辅助上游任务的识别.将事件抽取看作序列标注任务，构建了基于CRF多任务学习的中文事件抽取联合模型.针对仅基于CRF的事件抽取联合模型的缺陷进行了两个扩展：首先，采用分类训练策略解决联合模型中事件元素的多标签问题（即：当一个事件提及中包含多个事件时，同一个实体往往会在不同的事件中扮演不同的角色）.其次，由于处于同一事件大类下的事件子类，其事件元素存在高度的相互关联性.为此，提出采用多任务学习方法对各事件子类进行互增强的联合学习，进而有效缓解分类训练后的语料稀疏问题.在ACE 2005中文语料上的实验证明了该方法的有效性.



Abstract:Event extraction aims to extract the interesting and structured information from unstructured text. Most Chinese event extraction methods use a continuous pipeline model which first identify event trigger word, and then identify the event arguments. Thus, it is prone to produce cascading errors, and the information contained in downstream task cannot be fed back to the upstream task. In this study, event extraction is considered as a sequence labeling task, and a multi-task learning with CRF enhanced Chinese event extraction model is proposed. Two extensions on the CRF based event extraction model are performed:(1) the separate training strategy to solve multi-label problem for an event argument in the joint model (i.e., when an event scope includes multiple events, the same entity tends to play different roles in different events); (2) considered event arguments of sub-events under the same class have the high correlation, a multi-task learning approach is proposed to jointly learn sub-events, which can alleviate the corpus sparsity to some extent. The experiment results on ACE 2005 Chinese corpus show the effectiveness of the proposed method.



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