关键词: 意图, 正则化, 半监督, 文本聚类

Abstract: Aiming at the problem that the existing semi-supervised text clustering methods failing to simultaneously consider user intent in the process of representation learning and clustering. Deep Semi-supervised Text Clustering with Intentional Regularization (IRDSTC) was proposed. The intention regularization strategy was introduced, and the Intention Regularized Representation Learning (IRRL) module and Intention Regularized Clustering (IRC) module were designed. Firstly, an intent matrix is constructed based on the intent constraint information provided by the user to capture the user's expectations for the relationship between texts. Secondly, the matrix is applied to the representation learning stage and the clustering stage. In the representation learning stage, the intermediate layer representation extracted by the deep model was converted into a representation correlation matrix, and a regular term was constructed in conjunction with the intention matrix, aiming to incorporate user intention into representation learning. In the clustering stage, an allocation consistency matrix was constructed based on the cluster assignment probabilities obtained from clustering iterations, and combined with an intent matrix to construct regular terms to realize the guidance of the user intent on the clustering process. Finally, experimental results show that, the proposed model has better performance in clustering accuracy, normalized mutual information and adjusted rand index evaluation indexes compared to other clustering methods on Reu-10k, BBC, ACM and Abstract datasets. Compared with the suboptimal SDEC (Semi-supervised Deep Embedded Clustering), the normalized mutual information of IRDSTC is increased by 36.39%, 67.56%, 28.95%, and 20.76%, respectively, indicating that IRDSTC has better clustering effect.

Key words: intention, regularization, semi-supervised, text clustering