Sense-enhanced embeddings produce graphs that better capture semantic type information, allowing Neighbor Type Probability and Neighbor Type Entropy metrics to distinguish type matching from coercion in contextualized noun embeddings.
Learning a Vector-Based Model of A merican S ign L anguage Inflecting Verbs from Motion-Capture Data
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A graph-based analysis of semantic types and coercion in contextualized word embeddings
Sense-enhanced embeddings produce graphs that better capture semantic type information, allowing Neighbor Type Probability and Neighbor Type Entropy metrics to distinguish type matching from coercion in contextualized noun embeddings.