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We describe our entry to SemEval 2016 Task 10: Detecting Minimal Semantic Units and their Meanings. Our approach uses a discriminative first-order sequence model similar to Schneider and Smith (2015). The chief novelty in our approach is a factorization of the labels into multiword expression and supersense labels, and restricting first-order dependencies within these two parts. Our submitted models achieved first place in the closed competition (CRF) and second place in the open competition (2-CRF).

UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields