融合实体信息和时序特征的问答式事件检测方法
Question answering-based event detection method fusing entity information and temporal feature
马宇航 1宋宝燕 1丁琳琳 1鲁闻一 1纪婉婷1
作者信息
- 1. 辽宁大学 信息学院,辽宁 沈阳 110036
- 折叠
摘要
针对现有问答方法在处理触发词歧义性问题上的不足,提出一种融合实体信息和时序特征的问答式事件检测方法EDQA-EITF.构建一种基于RoBERTa的问答框架,增强模型的语义表示能力;通过在模型输入序列中显示地添加实体、实体类型等先验信息,进一步帮助模型根据句子的上下文语境对触发词进行分类;采用最小门控循环单元(minimal gated unit,MGU)和Transformer编码器对输入序列中的时序依赖关系进行建模,提升模型对于句子的语义关系、句法结构的阅读与理解能力.公共数据集上的实验结果表明,所提方法在进行事件检测时具有更优的性能,有效缓解了触发词的歧义性问题.
Abstract
Aiming at the shortcomings of existing question answering methods on the problem of trigger ambiguity,a question answering-based event detection method fusing entity information and temporal feature named EDQA-EITF was proposed.A question answering framework based on RoBERTa was constructed to enhance the model's semantic representation ability.The priori information such as entities and entity types was added to the model input sequence in a displayed way,to further help the model classify the triggers based on the contextual semantic environment of the sentence.The minimal gated unit(MGU)and Transformer encoder were used to model the temporal dependencies in the input sequence,which improved the model's ability to read and understand the semantic relation and syntactic structure of sentence.Experimental results on public dataset show that the proposed method has better performance in event detection and effectively alleviates the problem of trigger ambiguity.
关键词
事件检测/问答/RoBERTa/时序特征/先验信息/最小门控单元/TransformerKey words
event detection/question answering/RoBERTa/temporal feature/prior information/minimal gated unit/Trans-former引用本文复制引用
基金项目
辽宁省应用基础研究计划基金项目(2022JH2/101300250)
国家自然科学基金项目(62072220)
辽宁省中央引导地方科技发展基金项目(2022JH6/100100032)
辽宁省自然科学基金项目(2022-KF-13-06)
出版年
2024
NETL
NSTL
万方数据