Method for automatic correction of errors in annotated corpus using kernel Ripple-Down Rules

摘要

The present invention relates to a method of automatically modifying an error in a learning corpus for machine learning during a natural language process. According to the present invention, with existing corpus error modification methods, a user has to write a learning corpus in person for the generation of recognition and classification models, and thus, error patterns are irregular and rules for modification are not easy to make. To solve the problems, modification rules, reflecting properties of a document tagged from a correct corpus and an error corpus, are automatically generated through ripple-down rule (RDR), and an error in a learning corpus for machine learning is recognized to modify a morphological analysis corpus and an entity name corpus to minimize errors during mass production of corpuses, and moreover, properties of Korean corpuses are able to be applied through morphemic operation while a kernel is operated in an RDR system, and thus, changing only the kernel, the method is able to be applied to various tag corpuses.