A genetic programming approach for learning semantic information extraction rules from news

Abstract

Due to the increasing amount of data provided by news sources and the user specific information needs, recently, many news personalization systems have been proposed. Often, these systems process news data automatically into information, while relying on underlying knowledge bases, containing concepts and their relations for specific domains. For this, information extraction rules are frequently used, yet they are usually manually constructed. As it is difficult to efficiently maintain a balance between precision and recall, while using a manual approach, we present a genetic programming-based approach for automatically learning semantic information extraction rules from (financial) news that extract events. Our evaluation results show that compared to information extraction rules constructed by expert users, our rules yield a 27% higher F1-measure after the same amount of rules construction time.