PKU_HIT: An Event Detection System Based on Instances Expansion and Rich Syntactic Features
Shiqi Li, Pengyuan Liu, Tiejun Zhao, Qin Lu, Hanjing Li · 2010
This paper describes the PKU_HIT system on event detection in the SemEval-2010 Task. We construct three modules for the three sub-tasks of this evaluation. For target verb WSD, we build a Naïve Bayesian classifier which uses additional training instances expanded from an untagged Chinese corpus automatically. For sentence SRL and event detection, we use a feature-based machine learning method which makes combined use of both constituent-based and dependencybased features. Experimental results show that the Macro Accuracy of the WSD module reaches 83.81 % and F-Score of the SRL module is 55.71%.