基于序列到序列模型的事件识别

事件识别是以事件为单位进行信息抽取的起点,对后续各个子任务都意义重大。针对事件识别任务,该文提出了一种融入文档信息的序列到序列方法,一方面借助神经网络减少了特征工程产生的人工依赖,另一方面借助注意力机制将局部的词、实体与全局的文档中事件的共现等信息统一建模。在LDC2017E02语料上实验结果表明,该方法能有效提高事件识别的性能。

Event Detection Based on Sequence-to-Sequence Model

Event detection is the start point and impacts much on subsequent subtasks of information extraction. Towards better event detection, this paper proposes a sequence-to-sequence approach considering additional document-level information. It reduces the dependency on manual feature engineering by means of neural network, constructing an unified model combining local words, entities and global events co-occurrence in one document by the attention mechanism. Experimental results on LDC2017E02 corpus show that this method can effectively improve the performance of event recognition.