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Text Classification Aided Job Opportunity Mining

机译:文本分类辅助工作机会挖掘

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摘要

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.
机译:在本文中,我们提出了一部小说框架来分割中文单词,生成字向量,培训语料库并进行预测。基于文本分类技术,我们成功帮助中国残疾人在真正的单词中有效地获得了就业机会。结果表明,使用该方法构建分类器,比传统方法产生更好的结果。我们还通过实验表明,仔细选择要代表文档的特征子集可以提高分类器的性能。

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