Towards Learning Error-Driven Transformations for Information Extraction

Transformation-based and error-driven induction of rules has proven to be a successful and applicable technique for Part-of-Speech tagging and other natural language processing tasks. This paper presents the ongoing work to create a universal algorithm for learning transformations in the domain of information extraction and semantic annotation. Such a method can be applied to different and useful tasks in real world problems, e.g., correcting systematic labeling errors of statistical models or improving data sets annotated by humans. We show the applicability of the current prototype in the domain of reference segmentation, where our method is able to reduce the labeling errors of conditional random fields.