# CoarseWSD-20

The paper proposes a context-aware semantic similarity (CASS) method for unsupervised word sense disambiguation: given a target word, its context and an exclusion list, the context and the context with each candidate synonym substituted are encoded as vectors, and the synonym that minimizes the resulting semantic change (maximizes cosine similarity) is chosen as the sense decision; evaluated for accuracy on CoarseWSD-20, a Wikipedia-derived, noun-only benchmark of 20 words with 2 to 5 senses and 10,196 instances, against a random-option baseline (43.73%) and a most-frequent-sense baseline (73.43%), the BERT-embedding configuration reached 77.74% (7,927 hits), the only setting above the strong baseline, while USE (71.94%), ELMo (68.75%) and WMD (60.

The paper proposes a context-aware semantic similarity (CASS) method for unsupervised word sense disambiguation: given a target word, its context and an exclusion list, the context and the context with each candidate synonym substituted are encoded as vectors, and the synonym that minimizes the resulting semantic change (maximizes cosine similarity) is chosen as the sense decision; evaluated for accuracy on CoarseWSD-20, a Wikipedia-derived, noun-only benchmark of 20 words with 2 to 5 senses and 10,196 instances, against a random-option baseline (43.73%) and a most-frequent-sense baseline (73.43%), the BERT-embedding configuration reached 77.74% (7,927 hits), the only setting above the strong baseline, while USE (71.94%), ELMo (68.75%) and WMD (60.