基于多语义信息融合的事件检测模型

Event Detection Model Based on Semantic Information Fusion

[目的]通过融合多类语义信息,提高事件检测任务准确性。[方法]首先,利用Bi-LSTM模型编码非关系类语义信息;其次,基于关系类语义信息生成关系图,利用多尺度卷积神经网络捕获邻接矩阵蕴含的空间信息并与词向量进行融合;最后,构建Gated-GCN模型动态聚合并更新相邻词向量间的关系类语义信息,增强词向量的表征能力。[结果]基于ACE05基准数据集,与现有主流事件检测模型进行对比实验,所提模型的F1值达到76.3%,相较于最优的基准模型提升1.2个百分点。[局限]研究基于基准数据集,需要在一般的数据集上进行模型验证。[结论]融合多类语义信息能够有效提升事件检测性能。

[Objective]This paper aims to improve the accuracy of event detection tasks by fusing semantic information.[Methods]First,we stored the non-relational semantic information with an initial word vector and encoded them with the Bi-LSTM model to aggregate their contexts.Then,we developed a relation graph based on relational semantic information.Third,we used a multi-scale convolutional neural network to capture the spatial information from the adjacency matrix and fuse it with the word vector.Finally,we built a Gate-GCN model to aggregate relational semantic information between adjacent word vectors to enhance their representation ability.[Results]We examined the new model with the ACE05 benchmark dataset.Our method’s F1 value reached 76.3%,which was 1.2%higher than the existing mainstream models.[Limitations]The proposed model needs to be validated with general datasets.[Conclusions]Fusion of multiple types of semantic information can effectively improve the event detection performance.