@inproceedings{li-etal-2026-stream,
    title = "{STREAM}-{ZH}: Simplified Topic Retrieval Exploration and Analysis Module for {C}hinese Language",
    author = "Li, Hongyi  and
      Lian, Jianjun  and
      Thielmann, Anton Frederik  and
      Python, Andre",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-short.28/",
    pages = "371--383",
    ISBN = "979-8-89176-381-4",
    abstract = "We introduce Simplified Topic Retrieval Exploration and Analysis Module for Chinese language (STREAM-ZH), the first topic modeling package to fully support the Chinese language across a broad range of topic models, evaluation metrics, and preprocessing workflows. Tailored to both simplified and traditional Chinese language, our package extends the STREAM topic modeling framework with a curated collection of preprocessed textual datasets in Chinese from which we assess the performance of classical, neural, and clustering topic models using commonly-used intruder, diversity, and coherence metrics. The results of a benchmark analysis bring evidence that within our framework, topic models may generate coherent and diverse topics from datasets in Chinese language, outperforming those generated by topic models using English-translated textual input. Our framework facilitates multilingual accessibility and research in topic modeling applied to Chinese textual data. The code is available at the following link: https://github.com/AnFreTh/STREAM"
}