@inproceedings{kumar-etal-2020-online,
    title = "An Online Semantic-enhanced {D}irichlet Model for Short Text Stream Clustering",
    author = "Kumar, Jay  and
      Shao, Junming  and
      Uddin, Salah  and
      Ali, Wazir",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.acl-main.70/",
    doi = "10.18653/v1/2020.acl-main.70",
    pages = "766--776",
    abstract = "Clustering short text streams is a challenging task due to its unique properties: infinite length, sparse data representation and cluster evolution. Existing approaches often exploit short text streams in a batch way. However, determine the optimal batch size is usually a difficult task since we have no priori knowledge when the topics evolve. In addition, traditional independent word representation in graphical model tends to cause {\textquotedblleft}term ambiguity{\textquotedblright} problem in short text clustering. Therefore, in this paper, we propose an Online Semantic-enhanced Dirichlet Model for short sext stream clustering, called OSDM, which integrates the word-occurance semantic information (i.e., context) into a new graphical model and clusters each arriving short text automatically in an online way. Extensive results have demonstrated that OSDM has better performance compared to many state-of-the-art algorithms on both synthetic and real-world data sets."
}