WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models
Xin Lai Tang, Fei Li, Donghong Ji · 2016
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.