基于混合神经网络的实体和事件联合抽取方法

实体和事件抽取旨在从文本中识别出实体和事件信息并以结构化形式予以呈现。现有工作通常将实体抽取和事件抽取作为两个单独任务,忽略了这两个任务之间的紧密关系。实际上,事件和实体密切相关,实体往往在事件中充当参与者。该文提出了一种混合神经网络模型,同时对实体和事件进行抽取,挖掘两者之间的依赖关系。模型采用双向LSTM识别实体,并将在双向LSTM中获得的实体上下文信息进一步传递到结合了自注意力和门控卷积的神经网络来抽取事件。在英文ACE 2005语料库上的实验结果证明了该文方法优于目前最好的基准系统。

Joint Extraction of Entities and Events by a Hybrid Neural Network

Entity and event extraction aim at detecting entities and events from text, respectively. Previous studies in information extraction usually took entity extraction and event extraction as two separate tasks without capturing the close relationship between the two tasks. This paper proposes a hybrid neural network to simultaneously extract the entity and the event, and exploit the dependencies between them. This network first uses encoder-decoder bidirectional LSTM module to identify entities, and then introduces the entity context information from the above bidirectional LSTM module to a neural network, which combines self-attention and gated convolution to facilitate event extraction. Experimental results on the ACE 2005 English corpus show that our model outperforms the state-of-the-art methods.