Multiplicity and word sense: evaluating and learning from multiply labeled word sense annotations

Supervised machine learning methods to model word sense often rely on human labelers to provide a single, ground truth sense label for each word in its context. The finegrained, sense label inventories preferred by lexicographers have been argued to lead to lower annotation reliability in measures of agreement among two or three human labelers (annotators… CONTINUE READING