PKU: combining supervised classifiers with features selection

Authors:
Peng Jin;Danqing Zhu;Fuxin Li;Yunfang Wu
Affiliations:
Peking University, Beijing, China;Peking University, Beijing, China;Institute of Automation Chinese Academy of Sciences, Beijing, China;Peking University, Beijing, China
Venue:
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Year:
2007

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Abstract

This paper presents the word sense disambiguation system of Peking University which was designed for the SemEval-2007 competition. The system participated in the Web track of task 11 "English Lexical Sample Task via English-Chinese Parallel Text". The system is a hybrid model by combining two supervised learning algorithms SVM and ME. And the method of entropy-based feature chosen was experimented. We obtained precision (and recall) of 81.5%.