摘要
事件结构性语法特征与事件语义特征各有优势,二者融合利于准确表征事件触发词,进而有利于完成事件触发词抽取任务。现有的基于特征、基于结构及基于神经网络模型等的抽取方法仅能捕捉事件的部分特征,不能够准确表征事件触发词。为解决上述问题,提出一种融合了事件结构性语法特征和事件语义特征的混合模型,完成事件触发词抽取任务。首先,在初始化向量模型中融入句子的依存句法信息,使初始向量中包含事件结构性语法特征;然后,将初始向量依次传入神经网络模型中的CNN和BiGRU-E-attention模型中,在捕获多维度事件语义特征的同时,完成事件结构性语法特征与事件语义特征的融合;最后,进行事件触发词的抽取。在CEC中文突发语料库上进行事件触发词位置识别和分类实验,该模型的F值较基准模型的分别提高了0.86%和4.07%;在ACE2005英文语料库上,该模型的F值较基准模型的分别提高了1.4%和1.5%。实验结果表明,混合模型在事件触发词抽取任务中取得了优异的效果。
Although structural grammatical features and semantic features of events have their respective advantages, and the integration of the two features is conducive to accurately represent event trigger words and helpful to complete event trigger word extraction. However, existing feature-based, structure-based and neural network model-based extraction methods can only capture partial features of events, and cannot accurately represent event trigger words. In order to solve the above problems, a hybrid model combining event structural grammatical features with event semantic features is proposed to complete the task of event trigger word extraction. The hybrid model firstly integrates the sentence dependency syntax information into the initial vector model, so that the initial vector integrates the event structural grammatical features. Then, the initial vector is successively introduced into the CNN and BiGRU-E-attention models of the neural network model, and the events semantic features of multi-dimensional are captured. It also completes the feature fusion of event structural grammatical features and event semantic features, and finally completes the extraction of event trigger words. Experimental results on CEC Chinese Emergency Corpus show that the hybrid model improves the F values in the position recognition and classification tasks of event trigger words by 0.86% and 4.07%, respectively, compared with the baseline model. Experimental results on ACE2005 English corpus show that the hybrid model improves the F values in the position recognition and classification tasks of event trigger words by 1.4% and 1.5%, respectively, compared with the baseline model. The experimental results show that the hybrid model achieves excellent results in the task of event trigger word extraction.
作者
杨昊
赵刚
王兴芬
YANG Hao;ZHAO Gang;WANG Xing-fen(School of Information Management,Beijing Information Science&Technology University,Beijing 100192,China)
出处
《计算机工程与科学》
CSCD
北大核心
2023年第1期171-180,共10页
Computer Engineering & Science
基金
国家重点研发计划(2019YFB1405003)。
关键词
事件抽取
事件触发词
事件结构性语法特征
事件语义特征
特征融合
event extraction
event trigger word
event structural grammatical features
event semantic features
features fusion
