融合多跳关系标签与依存句法结构信息的事件检测模型

大部分基于依存句法分析的事件检测方法仅聚焦于依存句法结构上的单跳联系,忽视了词与词之间的多跳联系,造成事件触发词与部分相关实体间的语义缺失,从而影响了事件检测效率。因此,为了充分利用词语间的语义相关性提升事件触发词的识别能力,提出了融合多跳关系标签和依存句法结构信息的事件检测模型。构建了一种新型的依存句法多跳树以及多跳关系标签搜索算法,增强了核心词汇的事件表征能力,并结合图注意力网络聚合了词的多阶表示,提升了事件检测性能。在ACE2005数据集上的实验结果显示,提出的增加了多跳关系标签信息的事件检测方法比基准模型性能提升了近2%。

Event detection model based on integrating of multi-hop relation labels and dependency syntactic structure

Most of the event detection methods based on dependency parsing only focus on the single-hop connection of dependency syntax structure. It ignores the multi-hop connection between words, resulting in the lack of semantic information between trigger words and some related entities, which affects the efficiency of event detection. Therefore, this paper proposed an event detection model integrating multi-hop relation labels and dependency syntactic structure information to improve the recognition performance of event trigger words. This paper designed a new dependency syntax "multi hop tree" and multi hop relation label search algorithm to enhance the event-representation ability of core vocabulary. Furthermore, it used graph attention network to aggregate the multi-level word representation, which improved the event detection performance. Results on ACE2005 dataset show that this event detection method improves the performance of the benchmark model by nearly 2%.