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The absence of an appropriate text classification corpus makes the massiveamount of online job information unusable for labor market analysis. This paperpresents JCTC, a large job posting corpus for text classification. In JCTCconstruction framework, a formal specification issued by the Chinese centralgovernment is chosen as the classification standard. The unsupervised learning(WE-cos), supervised learning algorithm (SVM) and human judgements are all usedin the construction process. JCTC has 102581 online job postings distributed in465 categories. The method proposed here can not only ameliorate the highdemands on people's skill and knowledge, but reduce the subjective influencesas well. Besides, the method is not limited in Chinese. We benchmark fivestate-of-the-art deep learning approaches on JCTC providing baseline resultsfor future studies. JCTC might be the first job posting corpus for textclassification and the largest one in Chinese. With the help of JCTC, relatedorganizations are able to monitor, analyze and predict the labor market in acomprehensive, accurate and timely manner.

JCTC: A Large Job posting Corpus for Text Classification