Information Extraction Grammars

Abstract

Formal grammars are extensively used to represent patterns in Information Extraction, but they do not permit the use of several types of features. Finite-state transducers, which are based on regular grammars, solve this issue, but they have other disadvantages such as the lack of expressiveness and the rigid matching priority. As an alternative, we propose Information Extraction Grammars. This model, supported on Language Theory, does permit the use of several features, solves some of the problems of finite-state transducers, and has the same computational complexity in recognition as formal grammars, whether they describe regular or context-free languages.