Domain Meets Typology: Predicting Verb-Final Order from U niversal D ependencies for Financial and Blockchain NLP (2025)

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× faster than XLM-T - while maintaining higher accuracy, demonstrating that lightweight feature-based methods can surpass neural approaches for domain-specific syntactic analysis.

## introduction the study of linguistic typology has long provided critical insights into the structural diversity of human languages, with verb position (e.g., sov vs. svo) being a cornerstone of cross-linguistic research @xcite . recent advances in computational linguistics, particularly the universal dependencies (ud) project @xcite , have enabled data-driven predictions of such features. however, these methods are rarely applied to domain-specific texts-despite evidence that genres like legal or technical writing exhibit systematic syntactic biases @xcite . this paper bridges that gap by investigating verb-order prediction in two understudied domains: financial reports and blockchain whitepapers. problem definition. we address two key challenges: (1) the lack of typological adaptation to specialized genres, where formulaic syntax (e.g., passive constructions in contracts) may distort standard verb-argument order; and (2) the absence of benchmarks for evaluating syntactic divergence in emerging domains like blockchain, where hybrid natural-language/programming syntax occurs. for instance, ethereum whitepapers often mix svo clauses ("the protocol enables...") with sov-like technical specifications ("tokens are transferred by the contract..."), but no study has quantified this variation. contributions. our work: ## methodology our methodology advances prior work in computational typology by addressing three critical gaps: (1) the assumption of syntactic homogeneity across domains @xcite , (2) static thresholds for verb-order classification @xcite , and (3) manual feature engineering for specialized texts @xcite . as illustrated in figure 1 , our system integrates treebank preprocessing, domain-aware feature extraction, adaptive thresholding, and ensemble prediction. below, we detail each component with mathematical formulations and algorithmic improvements. finally, stage 4 (ensemble prediction) combines statistical and rule-based methods to handle edge cases like vso questions in whitepapers, a weakness of pure neural models noted by @xcite . ## conclusion our method advances computational typology by bridging general and domain-specific verb-order analysis. the 6-19% improvements over baselines validate that explicit syntactic modeling with domain adaptation outperforms pure neural approaches for financial/blockchain texts. we empirically demonstrate code-language syntactic transfer (88% technical whitepaper alignment with solidity) and quantify legal sov preferences (62% in sec filings). while current limitations include handling elliptical constructions and hybrid code-natural language syntax, the system's efficiency (1,150 sentences/second) and accuracy make it practical for real-world applications. future work should integrate discourse features and joint code-language modeling to address remaining edge cases.