基于NPN融入词性注意力机制的中文事件探测

A Chinese event detection method based on nugget proposal network with part-of-speech attention mechanism

事件探测主要研究触发词探测以及事件类型识别。现阶段基于深度学习的模型大部分集中在利用语义角色信息、句法依存树信息以及预训练模型方面,忽略了词性的重要性。针对这个问题,提出基于块提取网络融入词性注意力机制的中文事件探测方法,首先基于NLP词性标注工具获得词性序列,然后使用CBOW算法获得词性嵌入,最后在模型中使用词性嵌入计算词性注意力用于事件探测。在ACE2005数据集上进行实验,融入词性注意力后模型在事件探测任务上的F1分数分别提升了3.8%和2.4%,表明了该方法的有效性。

Event detection mainly studies trigger word detection and event type recognition.At pre-sent,most models based on deep learning focus on semantic role information,syntactic dependency tree information and pre-training models,but ignore the importance of parts of speech.To solve this problem,this paper proposes a Chinese event detection method based on nugget proposal network with part-of-speech attention mechanism.The method firstly obtains part-of-speech sequence based on NLP part-of-speech tagging tool,then uses the CBOW algorithm to obtain part-of-speech embedding,and finally uses part-of-speech embedding in the model to calculate part-of-speech attention for event detection.Experiments on the ACE2005 show that the F1 score of the model with part-of-speech attention is improved by 3.8%and 2.4%respectively on the event detection task,which proves the effectiveness of the method.