Information Technology Journal1812-56381812-5646Asian Network for Scientific Information10.3923/itj.2010.184.187FuJianfeng LiuZongtian ZhongZhaoMan ShanJianfang 1201091In this study, we focus on the two subtasks of Chinese event extraction: (1) Chinese event detection and identification; (2) Chinese event argument extraction. Some features for the two subtasks are provided. Considering the particular contributions of different features on classification analysis in the subtasks, we weight features by introducing ReliefF algorithm. Experimental results show that comparing with normal K-Nearest Neighbor algorithm, feature weighting obviously improves the F-Measures in Chinese event detection and identification and Chinese event argument extraction.]]>Ahn, D.,20062006pp: 18Chen, Z. and H. Ji,20092009pp: 209212Consortium, L.D.,20052005Modha, D.S. and W.S. Spangler,2003k-means clustering.]]>52217237Robnik-Sikonja, M. and I. Kononenko,2003532369Tan, H., T. Zhao and J. Zheng,20082008pp: 1419Wettschereck, D., D. Aha and T. Mohri,199711273314Sun, Y.,20072910351051