Original Contribution: Stacked generalization
Neural Networks
Pairwise classification and support vector machines
Advances in kernel methods
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Multiclass text categorization for automated survey coding
Proceedings of the 2003 ACM symposium on Applied computing
Text chunking by combining hand-crafted rules and memory-based learning
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Japanese zero pronoun resolution based on ranking rules and machine learning
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A Web-Based Automated System for Industry and Occupation Coding
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
Estimation of class membership probabilities in the document classification
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Hi-index | 0.00 |
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 yields even better accuracy.