Automatic Occupation Coding with Combination of Machine Learning and Hand-Crafted Rules

Abstract. We apply a machine learning method to the occupation coding, which is a task to categorize the answers to open-ended questions regarding the respondent’s occupation. Specifically, we use Support Vector Machines (SVMs) and their combination with hand-crafted rules. Conducting the occupation coding manually is expensive and sometimes leads to inconsistent coding results when the coders are not experts of the occupation coding. For this reason, a rule-based automatic method has been developed and used. However, its categorization performance is not satisfiable. Therefore, we adopt SVMs, which show high performance in various fields, and compare it with the rule-based method. We also investigate effective combination methods of SVMs and the rule-based method. In our methods, the output of the rule-based method is used as features for SVMs. We empirically show that SVMs outperform the rule-based method in the occupation coding and that the combination of the two methods yiel...

• Type: Conference
• Year: 2005
• Where: PAKDD
• Authors: Kazuko Takahashi, Hiroya Takamura, Manabu Okumura
• Keywords: Data Mining, Occupation Coding, PAKDD 2005, Rule-based Automatic Method, Rule-Based Method