Abstract: 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.

Key words: event extraction, event trigger word, event structural grammatical features, event semantic features, features fusion