Abstract
In this paper, we propose a weakly-supervised occupation detection approach which can automatically detect occupation information for micro-blogging users. The weakly-supervised approach makes use of two types of user information (tweets and personal descriptions) through a rule-based user occupation detection and a MCS-based (MCS: a multiple classifier system) user occupation detection. First, the rule-based occupation detection uses the personal descriptions of some users to create pseudo-training data. Second, based on the pseudo-training data, the MCS-based occupation detection uses tweets to do further occupation detection. However, the pseudo-training data is severely skewed and noisy, which brings a big challenge to the MCS-based occupation detection. Therefore, we propose a class-based random sampling method and a cascaded ensemble learning method to overcome these data problems. The experiments show that the weakly-supervised occupation detection achieves a good performance. In addition, although our study is made on Chinese, the approach indeed is language-independent.
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Chen, Y., Pei, B. (2014). Weakly-Supervised Occupation Detection for Micro-blogging Users. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_27
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DOI: https://doi.org/10.1007/978-3-662-45924-9_27
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