@inproceedings{khatib-etal-2025-clutch,
    title = "``Clutch or Cry'' Team at {TRACS} @ {WASP}2025: A Hybrid Stacking Ensemble for Astrophysical Document Classification",
    author = "Khatib, Arshad  and
      Prasad, Aayush  and
      Trivedi, Rudra  and
      Malviya, Shrikant",
    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.17/",
    pages = "146--156",
    ISBN = "979-8-89176-310-4",
    abstract = "Automatically identifying telescopes and their roles within astrophysical literature is crucial for large-scale scientific analysis and tracking instrument usage patterns. This paper describes the system developed by the ``Clutch or Cry'' team for the Telescope Reference and Astronomy Categorization Shared task (TRACS) at WASP 2025. The task involved two distinct challenges: multi-class telescope identification (Task 1) and multi-label role classification (Task 2). For Task 1, we employed a feature-centric approach combining document identifiers, metadata, and textual features to achieve high accuracy. For the more complex Task 2, we utilized a carefully designed two-level stacking ensemble. This hybrid model effectively fused symbolic information from a rule-based classifier with deep semantic understanding from a domain-adapted transformer. A subsequent meta-learning stage then performed targeted optimization for each role. These architectures were designed to address the primary challenges of handling long documents and managing severe class imbalance. A systematic optimization strategy focused on mitigating this imbalance significantly improved performance for minority classes. This work validates the effectiveness of using tailored, hybrid approaches and targeted optimization for complex classification tasks in specialized scientific domains."
}