基于半监督学习的中文社交文本事件聚类方法(Semi-supervised Method to Cluster Chinese Events on Social Streams)

CCL 2020 · Hengrui Guo, Zhongqing Wang, Peifeng Li, Qiaoming Zhu ·

面向社交媒体的事件聚类旨在根据事件特征对短文本聚类。目前,事件聚类模型主要分为无监督模型和有监督模型。无监督模型聚类效果较差,有监督模型依赖大量标注数据。基于此,本文提出了一种半监督事件聚类模型(SemiEC),该模型在小规模标注数据的基础上,利用LSTM表征事件,利用线性模型计算文本相似度,进行增量聚类,利用增量聚类产生的标注数据对模型再训练,结束后对不确定样本再聚类。实验表明,SemiEC的性能相比其他模型均有所提高。

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基于半监督学习的中文社交文本事件聚类方法(Semi-supervised Method to Cluster Chinese Events on Social Streams)

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