WHUNLp at SemEval-2016 task DiMSUM: A pilot study in detecting minimal semantic units and their meanings using supervised models

Abstract

This paper describes our approach towards the SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiM-SUM). 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 Word-Net 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.