Classification of Responses to Open-ended Questions with Machine Learning and Hand-Crafted Rules :

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  • ルールベース手法と機械学習による自由回答の分類
  • ルールベース手法と機械学習による自由回答の分類--職業コーディング自動化の方法
  • ルールベース シュホウ ト キカイ ガクシュウ ニ ヨル ジユウ カイトウ ノ ブンルイ ショクギョウ コーディング ジドウカ ノ ホウホウ
  • ―職業コーディング自動化の方法―
  • Automatic Occupation Coding Methods

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     We apply both a rule-based method and a machine learning method to the occupation coding, which is a task to categorize the answers to open-ended questions about the respondent's occupation. Specifically, we use Support Vector Machines (SVMs). 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 empirically show that SVMs outperform the rule-based method in the occupation coding with JGSS(Japanese General Social Surveys) data set. These two methods can be expanded to apply to responses to open-ended questions similar to occupation data.

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