Job Opportunity Mining on Web
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.
word segmentation SVM TFIDF word vector
Shilin Zhang Mei Gu
Faculty of Computer Science, Network and Information Management Center North China University of Technology Beijing, China
国际会议
桂林
英文
199-203
2010-11-17（万方平台首次上网日期，不代表论文的发表时间）