Author/Authors :
QIN, Bing Harbin Institute of Technology - School of Computer Science and Technology, Research Center for Information Retrieval, China , ZHAO, Yanyan Harbin Institute of Technology - School of Computer Science and Technology, Research Center for Information Retrieval, China , DING, Xiao Harbin Institute of Technology - School of Computer Science and Technology, Research Center for Information Retrieval, China , LIU, Ting Harbin Institute of Technology - School of Computer Science and Technology, Research Center for Information Retrieval, China , ZHAI, Guofu Harbin Institute of Technology - School of Electrical and Information Engineering, China
Abstract :
Event extraction is an important research point in information extraction, which includes two important sub-tasks of event type recognition and event argument recognition. This paper describes a methodbased on automatic expansion of the event triggers for event type recognition. The event triggers are first extended through a thesaurus to enable the extraction of the candidate events and their candidate types.Then, a binary classification method is used to recognize the candidate event types. This method effectively improves the unbalanced data problem in training models and the data sparseness problem with a small corpus. Evaluations on the ACE2005 dataset give a final F-score of 61.24%, which outperforms traditional methods based on pure machine learning.
