Abstract: Event detection is an important research direction in the field of information extraction.The existing event detection models are limited by the training targets of language models,and the dependency relationship between words can only be acquired passively,so the models pay more attention to the unrelated components during training,resulting in the wrong decetion results.Previous studies show that fully understanding contextual information is crucial for deep learning-based event detection techniques.In this paper,we introduce the KVMN network to capture the dependencies between words and enhance the semantic features of words,and a gating mechanism is adapted to weight these features.Then,in order to solve the problem of the model’sidentification of wrong decisions,negative samples are added to the input,and different levels of noise are added for different samples,so that the model could learn a better embedding representation,effectively improving the model’s ability to generalise unknown samples.Finally,experimental results on the public dataset LEVEN show that this method is superior to the existing methods and achieves a F1 score of 93.43%.

Key words: Event detection, Dependency, Key-value memory network, Gating mechanism, Negative sampling