@inproceedings{zheng-etal-2025-fnscc,
    title = "{FNSCC}: Fuzzy Neighborhood-Aware Self-Supervised Contrastive Clustering for Short Text",
    author = "Zheng, Zijian  and
      Lu, Yonghe  and
      Yin, Jian",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.154/",
    doi = "10.18653/v1/2025.findings-emnlp.154",
    pages = "2831--2846",
    ISBN = "979-8-89176-335-7",
    abstract = "Short texts pose significant challenges for clustering due to semantic sparsity, limited context, and fuzzy category boundaries. Although recent contrastive learning methods improve instance-level representation, they often overlook local semantic structure within the clustering head. Moreover, treating semantically similar neighbors as negatives impair cluster-level discrimination. To address these issues, we propose Fuzzy Neighborhood-Aware Self-Supervised Contrastive Clustering (FNSCC) framework. FNSCC incorporates neighborhood information at both the instance-level and cluster-level. At the instance-level, it excludes neighbors from the negative sample set to enhance inter-cluster separability. At the cluster-level, it introduces fuzzy neighborhood-aware weighting to refine soft assignment probabilities, encouraging alignment with semantically coherent clusters. Experiments on multiple benchmark short text datasets demonstrate that FNSCC consistently outperforms state-of-the-art models in accuracy and normalized mutual information. Our code is available at \url{https://github.com/zjzone/FNSCC}."
}