@inproceedings{li-ke-2025-domain,
    title = "Domain Meets Typology: Predicting Verb-Final Order from {U}niversal {D}ependencies for Financial and Blockchain {NLP}",
    author = "Li, Zichao  and
      Ke, Zong",
    editor = "Hahn, Michael  and
      Rani, Priya  and
      Kumar, Ritesh  and
      Shcherbakov, Andreas  and
      Sorokin, Alexey  and
      Serikov, Oleg  and
      Cotterell, Ryan  and
      Vylomova, Ekaterina",
    booktitle = "Proceedings of the 7th Workshop on Research in Computational Linguistic Typology and Multilingual NLP",
    month = aug,
    year = "2025",
    address = "Vinenna. Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.sigtyp-1.15/",
    pages = "156--164",
    ISBN = "979-8-89176-281-7",
    abstract = "This paper introduces a domain-adapted approach for verb-order prediction across general and specialized texts (financial/blockchain), combining Universal Dependencies syntax with novel features (AVAR, DLV) and dynamic threshold calibration. We evaluate on 53 languages from UD v2.11, 12K financial sentences (FinBench), and 1,845 blockchain whitepapers (CryptoUD), outperforming four baselines by 6-19{\%} F1. Key findings include: (1) 62{\%} SOV prevalence in SEC filings (+51{\%} over general English), (2) 88{\%} technical whitepaper alignment with Solidity{'}s SOV patterns, and (3) 9{\%} gains from adaptive thresholds. The system processes 1,150 sentences/second - 2.4{\texttimes} faster than XLM-T - while maintaining higher accuracy, demonstrating that lightweight feature-based methods can surpass neural approaches for domain-specific syntactic analysis."
}