PKU_HIT: An event detection system based on instances expansion and rich syntactic features

Authors:
Shiqi Li;Pengyuan Liu;Tiejun Zhao;Qin Lu;Hanjing Li
Affiliations:
Harbin Institute of Technology, Harbin, China;Peking University, Beijing, China;Harbin Institute of Technology, Harbin, China;The Hong Kong Polytechnic University, Hong Kong, China;Harbin Institute of Technology, Harbin, China
Venue:
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Year:
2010

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Abstract

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 dependency-based 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%.