@inproceedings{nguyen-etal-2025-glocom,
    title = "{G}lo{COM}: A Short Text Neural Topic Model via Global Clustering Context",
    author = "Nguyen, Quang Duc  and
      Nguyen, Tung  and
      Nguyen, Duc Anh  and
      Van, Linh Ngo  and
      Dinh, Sang  and
      Nguyen, Thien Huu",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.naacl-long.51/",
    pages = "1109--1124",
    ISBN = "979-8-89176-189-6",
    abstract = "Uncovering hidden topics from short texts is challenging for traditional and neural models due to data sparsity, which limits word co-occurrence patterns, and label sparsity, stemming from incomplete reconstruction targets. Although data aggregation offers a potential solution, existing neural topic models often overlook it due to time complexity, poor aggregation quality, and difficulty in inferring topic proportions for individual documents. In this paper, we propose a novel model, **GloCOM** (**Glo**bal **C**lustering C**O**ntexts for Topic **M**odels), which addresses these challenges by constructing aggregated global clustering contexts for short documents, leveraging text embeddings from pre-trained language models. GloCOM can infer both global topic distributions for clustering contexts and local distributions for individual short texts. Additionally, the model incorporates these global contexts to augment the reconstruction loss, effectively handling the label sparsity issue. Extensive experiments on short text datasets show that our approach outperforms other state-of-the-art models in both topic quality and document representations."
}