Event detection by combining self-attention and CNN-BiGRU

Event detection methods based on convolutional neural networks and recurrent neural networks have been widely investigated. However, convolutional neural networks only consider local information within the convolution window, ignoring the context of words. Recurrent neural networks have the problem of vanishing gradient and short-term memory, and their variant gated recurrent units cannot get the features of each word. Therefore, in this paper, an event detection method based on self-attention and convolutional bidirectional gated recurrent units model is proposed, which takes both word vectors and position vectors as inputs. It can not only extract vocabulary level features with different granularities by convolutional neural network and sentence level features by bidirectional gated recurrent units, but also consider global information and pay attention to more important features for event detection by self-attention. The extracted lexical-level features and sentence-level features are combined as the joint features, and the candidate words are classified by the softmax classifier to complete the event detection task. Experimental results show that the F scores of trigger words recognition and classification reach 78. 9% and 76. 0% respectively on the ACE2005 English corpus, which are better than the results of benchmark methods. Furthermore, the model shows great convergence. It is shown that the proposed model based on self-attention and convolutional bidirectional gated recurrent units possesses good ability of text feature extraction and improves the performance of event detection. © 2022 Science Press. All rights reserved.