English Article Correction System Using Semantic Category Based Inductive Learning Rules

Authors:
Hokuto Ototake;Kenji Araki
Affiliations:
Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan 060-0814;Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan 060-0814
Venue:
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Year:
2009

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Abstract

In this paper, we describe a system for automatic correction of English. Our system uses rules based on article context features, and generates new abstract rules by Semantic Category Based Inductive Learning that we proposed before. In the experiments, we achieve 93% precision with the best set of parameters. This method scored higher than our previous system, and is competitive with a related method for the same task.