TreeMatch: A Fully Unsupervised WSD System Using Dependency Knowledge on a Specific Domain
This work addresses domain-specific Word Sense Disambiguation (WSD), proposing the first fully unsupervised, dependency-knowledge-driven framework. Unlike conventional supervised or semi-supervised approaches that rely on annotated corpora or general-purpose lexical resources (e.g., WordNet), our method leverages only domain-customized dependency relations extracted from a domain-specific knowledge base to model word semantics. It performs unsupervised semantic similarity computation guided by dependency structures and conducts coarse-grained sense matching—both without any human-annotated training data or external dictionaries. The key contribution lies in the explicit integration of domain-specific dependency knowledge into the WSD pipeline, enabling effective sense discrimination in a purely unsupervised setting. Evaluated on the SemEval 2010 Task 17 benchmark, our approach substantially outperforms the First Sense Baseline, demonstrating both the efficacy and transferability of domain dependency knowledge for unsupervised WSD.