.png){kind=icon}
.png){kind=icon}
Return
Semi-Supervised Short Text Stream Clustering With Dual-View Semantic Representation and Cluster-Complement Learning
.png){kind=icon}
PRE
AI
Y
P
DOI：10.1109/tbdata.2026.3679463.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Short text stream clustering is a challenging task since 1) the limited words in short texts lead to text sparsity, making it difficult to capture the comprehensive semantic information of texts, and 2) the topic evolution leads to catastrophic forgetting of the learning model, causing a degradation in clustering performance. To address these issues, we propose a semi-supervised short text stream clustering method with Dual-View semantic representation and Cluster-Complement learning (DVCC). Specifically, to address insufficient semantic information caused by text sparsity, we introduce dual-view semantic representation, consisting of the word-level semantic view and the sentence-level semantic view. We model the word-level semantic view using the Dirichlet Process Multinomial Mixture (DPMM) model and generate the sentence-level semantic view by an encoder. These two views are then integrated into a unified model for online clustering via a Bernoulli random variable. To address catastrophic forgetting caused by topic evolution, we introduce cluster-complement learning to update the encoder offline periodically. We initially use a small amount of labeled data to initialize the parameters of the encoder, and then use a large amount of unlabeled texts and their pseudo-labels as training data for subsequent updates. The proposed cluster-complement learning supplements the training data with cluster centroids, ensuring the centroids of all active clusters appear in each batch of data to maintain knowledge of old topics. Finally, we validate the effectiveness of our method by conducting extensive experiments on six benchmark datasets. The experimental results show that DVCC outperforms existing state-of-the-art methods.
Keywords:
Short text stream clustering
dual-view semantic representation
cluster-complement learning
Journal
I
IF:
5.7
Papers:
834
Citations:
3.0K
Organization
Cited Papers