A Template-based Information Extraction System for Text Understanding

Abstract

This paper presents a template-based information extraction system for Arabic descriptive text understanding. The system depends on knowledge base. The knowledge base contains facts and rules. The facts are derived from AL Khalil lexicon, Al Ramous lexicon and a Stanford model. The rules represent the designed templates. The templates are helpful for detecting the meaning of the text. the inference engine depends on the hybrid chaining to fill the slots in templates from the text. The semantic criterion is augmented to the templates. the criterion calculates the frequency of the template in the text. the system is tested on Arabic texts taken in oil production domain from Arabic news website as Arabic CNN, and Arabic BBC. The system implements good response in getting the goal of descriptive text. Text understanding is made efficiency, and high accuracy is obtained.