TEG-a hybrid approach to information extraction

Abstract This paper describes a hybrid statistical and knowledge-based inforQ1 mation 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 labour 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 system does not contain any purely linguistic components, such as PoS tagger or shallow parser, but allows to using external linguistic components if necessary. We demonstrate the performance of the system on several named entity extraction and relation extraction tasks. The experiments show that our hybrid approach outperforms both purely statistical and purely know...

• Type: Journal
• Year: 2006
• Where: KAIS
• Authors: Ronen Feldman, Binyamin Rosenfeld, Moshe Fresko
• Keywords: KAIS 2006, Knowledge-based Systems, Mation Extraction Model, Trainable Extraction Grammar