Meta-learning beyond classification: A framework for information extraction from the Web

This paper proposes a meta-learning framework in the context of information extraction from the Web. The proposed framework relies on learning a meta-level classifier, based on the output of base-level information extraction systems. Such systems are typically trained to recognize relevant information within documents, i.e., streams of lexical units, which differs significantly from the task of classifying feature vectors that is commonly assumed for metalearning. The proposed framework was evaluated experimentally on the challenging task of training an information extraction system for multiple Web sites. Three well-known methods for training extraction systems were employed at the base level. A variety of classifiers were comparatively evaluated at the meta level. The extraction accuracy that was obtained demonstrated the effectiveness of the proposed framework of collaboration between base-level extraction systems and common classifiers at meta-level.