Improved Text Classification to Acquire Job Opportunities for Chinese Disabled Persons

摘要：

Text Classification is an important field of research. There are a number of approaches to classify text documents. However, there is an important challenge to improve the computational efficiency and recall. In this paper, we propose a novel framework to segment Chinese words, generate word vectors, train the corpus and make prediction. Based on the text classification technology, we successfully help the Chinese disabled persons to acquire job opportunities efficiently in real word. The results show that using this method to build the classifier yields better results than traditional methods. We also experimentally show that careful selection of a subset of features to represent the documents can improve the performance of the classifiers.

关键词： Word segmentation SVM TFIDF Word Vector

作者: Shilin Zhang Mei Gu

作者单位: Faculty of Computer Science Network and Information Management Center North China University of Technology Beijing China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 22-26

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）