2026
EACL
EACL 2026
Onomasiological Sense Alignment Across Dialect Dictionaries. A Taxonomy-Constrained LLM Classification
Abstract
AbstractWe propose a taxonomy-guided approach to semantic alignment that assigns lexicographic senses to an onomasiological taxonomy derived from the Hallig–Wartburg/Post system. Using an LLM under strict taxonomic constraints, short and heterogeneous meaning descriptions are assigned to a common conceptual space. Evaluation against expert annotation shows that run-to-run model agreement (kappa = 0.73) closely matches human agreement (kappa = 0.74), with robustness at coarse taxonomic levels and predictable degradation at finer granularity. A qualitative network analysis demonstrates the resulting potential for cross-dictionary exploration of dialectal variation in semantics.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio