Deep Contrastive Siamese Network Based Repeated Event Identification

In China,citizens can report issues they encounter in daily life to the government and seek assistance by calling the 12345 citizen hotline.However,many events are reported multiple times,which places significant pressure on the staffs responsible for event allocation,resulting in low efficiency of event disposal and waste of public resources.Identifying repeated events requires precise analysis of textual semantics and contextual relationships.To address this problem,this paper proposes an event repetition identification method based on a deep contrastive siamese network.By evaluating the similarity between the descriptions of events,the method identifies events with the same demands.First,it reduces the number of events through retrieval and filtering.Then,it fine-tunes a pre-trained BERT model through contrastive learning to learn distinct semantic representations of event descriptions.Finally,the event title is introduced as contextual information,and a siamese network with a classifier is used to identify repeated events.Experimental results on the 12345 event dataset of Nantong demonstrate that the proposed method outperforms baseline methods across various evaluation metrics,particularly in the F0.5 score,which is relevant to the repetition task scenario.The proposed method can effectively identify repeated events and improve the efficiency of event handling.