@inproceedings{kiet-etal-2025-systematic,
    title = "Systematic Evaluation of Machine Learning and Transformer-Based Methods for Scientific Telescope Literature Classification",
    author = "Kiet, Huynh Trung  and
      Minh, Dao Sy Duy  and
      Nguyen, Tran Chi  and
      Quy, Nguyen Lam Phu  and
      Hoa, Pham Phu  and
      Duong, Nguyen Dinh Ha  and
      Dien, Dinh  and
      Long, Nguyen Hong Buu",
    editor = "Accomazzi, Alberto  and
      Ghosal, Tirthankar  and
      Grezes, Felix  and
      Lockhart, Kelly",
    booktitle = "Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications",
    month = dec,
    year = "2025",
    address = "Mumbai, India and virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.wasp-main.16/",
    pages = "136--145",
    ISBN = "979-8-89176-310-4",
    abstract = "Recent space missions such as Hubble, Chandra, and JWST have produced a rapidly growing body of scientific literature. Maintaining telescope bibliographies is essential for mission assessment and research traceability, yet current curation processes rely heavily on manual annotation and do not scale. To facilitate progress in this direction, the TRACS @ WASP 2025 shared task provides a benchmark for automatic telescope bibliographic classification based on scientific publications. In this work, we conduct a comparative study of modeling strategies for this task. We first explore traditional machine learning methods such as multinomial Naive Bayes with TF{--}IDF and CountVectorizer representations. We then evaluate transformer-based multi-label classification using BERT-based scientific language models. Finally, we investigate a task-wise classification approach, where we decompose the problem into separate prediction tasks and train a dedicated model for each. In addition, we experiment with a limited-resource LLM-based approach, showing that even without full fine-tuning and using only a partial subset of the training data, LLMs exhibit promising potential for telescope classification. Our best system achieves a macro F1 of 0.72 with BERT-based models on the test evaluation, substantially outperforming the official openai-gpt-oss-20b baseline (0.31 macro F1)."
}