  The absence of an appropriate text classification corpus makes the massive
amount of online job information unusable for labor market analysis. This paper
presents JCTC, a large job posting corpus for text classification. In JCTC
construction framework, a formal specification issued by the Chinese central
government is chosen as the classification standard. The unsupervised learning
(WE-cos), supervised learning algorithm (SVM) and human judgements are all used
in the construction process. JCTC has 102581 online job postings distributed in
465 categories. The method proposed here can not only ameliorate the high
demands on people's skill and knowledge, but reduce the subjective influences
as well. Besides, the method is not limited in Chinese. We benchmark five
state-of-the-art deep learning approaches on JCTC providing baseline results
for future studies. JCTC might be the first job posting corpus for text
classification and the largest one in Chinese. With the help of JCTC, related
organizations are able to monitor, analyze and predict the labor market in a
comprehensive, accurate and timely manner.
