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This paper describes our approach towards the SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM). We consider that the two problems are similar to multiword expression detection and supersense tagging, respectively. The former problem is formalized as a sequence labeling problem solved by first-order CRFs, and the latter one is formalized as a classification problem solved by Maximum Entropy Algorithm. To carry out our pilot study quickly, we extract some simple features such as words or part-of-speech tags from the training set, and avoid using external resources such as WordNet or Brown clusters which are allowed in the supervised closed condition. Experimental results show that much further work on feature engineering and model optimization needs to be explored.

WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models