太赫兹科学与电子信息学报2023，Vol.21Issue(12)：1464-1475,12.DOI:10.11805/TKYDA2021438
面向句义及句法的事件检测模型
Event detection with joint learning of semantic and syntactic representation
柏瑶 1刘丹 1郭又铭 2李美文2
作者信息
- 1. 电子科技大学 电子科学技术研究院,四川 成都 611731
- 2. 电子科技大学 格拉斯哥学院,四川 成都 611731
- 折叠
摘要
Abstract
The syntactic structure of event sentences contributes to semantic understanding.A novel event detection model called BERT(Bidirectional Encoder Representations from Transformers)+D(Dependency)-T(Tree)-LSTM(Long Short-Term Memory network)+D-Attention(BDD)is proposed,which aims to learn semantic and syntactic representation of sentences jointly to enhance the event-sentence understanding ability.Taking the word vector based on BERT as the information source,D-T-LSTM model is designed to integrate the learning of syntactic structure and sentence semantics.An attention mechanism based on the dependency vector is added to strengthen the distinction of different syntactic structures at the aim of event detection.Experiment results on the Chinese Emergency Corpus(CEC)prove the effectiveness of BDD.The precision,recall and F1 value of BDD are rather optimum,and the F1 value is 5.4%higher than that of the benchmark model,and the recall rate is 0.4%higher.关键词
事件检测/来自变压器的双向编码器表示/基于依存树的长短时记忆网络模型/基于依存向量的注意力机制Key words
event detection/Bidirectional Encoder Representations from Transformers/Dependency-Tree-LSTM(Long Short-Term Memory Network)/D-Attention分类
信息技术与安全科学引用本文复制引用
柏瑶,刘丹,郭又铭,李美文..面向句义及句法的事件检测模型[J].太赫兹科学与电子信息学报,2023,21(12):1464-1475,12.
