@article{37,
  author = {Jianfeng Fu},
  title = {Feature weighting based Semi-supervised Clustering for Extracting Event Argument},
  journal = {Journal of Information Technology Review},
  year = {2010},
  volume = {1},
  number = {1},
  doi = {},
  url = {http://www.dline.info/jitr/fulltext/Paper-05.pdf},
  abstract = {In the area of information extraction, event extraction is a challenging task which attracts much attention. This paper focuses on its subtask, event argument extraction, and presents a semi-supervised clustering method for extracting event argument. Considering the particular contributions of different features on clustering analysis, the method weights features by introducing the ReliefF algorithm. Constrained-KMeans, a semi-supervised clustering algorithm, is employed to group event arguments. Compared with normal Constrained-KMeans algorithm, feature weighting obviously improves the F-Measure of system performance. The comprehensive experimental results also demonstrate the outstanding performance of our method.},
}