PKU: Combining Supervised Classifiers with Features Selection
نویسندگان
چکیده
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%.
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