document: Hybrid semantic tagging for information extraction The semantic web is expected to have an impact at least as big as that of the existing HTML based web , if not greater . However , the challenge lays in creating this semantic web and in converting existing web information into the semantic paradigm . One of the core technologies that can help in migration process is automatic markup , the semantic markup of content , providing the semantic tags to describe the raw content . This paper describes a hybrid statistical and knowledge-based information extraction model , able to extract entities and relations at the sentence level . The model attempts to retain and improve the high accuracy levels of knowledge-based systems while drastically reducing the amount of manual labor by relying on statistics drawn from a training corpus . The implementation of the model , called TEG ( Trainable Extraction Grammar ) , can be adapted to any IE domain by writing a suitable set of rules in a SCFG ( Stochastic Context Free Grammar ) based extraction language , and training them using an annotated corpus . The experiments show that our hybrid approach outperforms both purely statistical and purely knowledge-based systems , while requiring orders of magnitude less manual rule writing and smaller amount of training data . We also demonstrate the robustness of our system under conditions of poor training data quality . This makes the system very suitable for converting legacy web pages to semantic web pages .
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