基于深度对比孪生网络的事件辨重方法

Deep Contrastive Siamese Network Based Repeated Event Identification

在中国,市民可以通过拨打12345市民热线,向政府报告生活中遇到的问题并寻求帮助。然而,有许多重复的事件被多次上报,这给负责事件分派的工作人员带来了很大的压力,也会导致事件的处置效率变低,浪费社会公共资源。对重复事件的判断需要精确分析文本语义和上下文关系,为了解决这个问题,文中提出了一种基于深度对比孪生网络的事件辨重方法,通过评估两个事件的描述文本之间的相似性,辨别出具有相同诉求的事件。首先通过召回和过滤的方法来减少候选事件的数量;然后通过对比学习构造任务,微调预训练的BERT模型,学习易于辨识的事件描述语义表征;最后引入事件标题作为上下文信息,并通过带有分类器的孪生网络来识别重复事件。在南通市12345事件数据集上进行了实验,结果表明,该方法在各项评估指标上均优于基线方法,特别是在与辨重任务场景相关的F0.5分数上,能够有效地辨别重复事件,提高事件处置的效率。

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.