Papers by Dan Edelstein
Rethinking Word Similarity: Semantic Similarity through Classification Confusion (2025.naacl-long)
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| Challenge: | Word similarity measures cannot capture context-dependent, asymmetrical, polysemous nature of semantic similarity. |
| Approach: | They propose a new measure of similarity that reframes semantic similarity in terms of feature-based classification confusion. |
| Outcome: | The proposed model is comparable to cosine similarity in matching human similarity judgments across several datasets and can measure similarity using predetermined features of interest. |