TAILIEUCHUNG - Báo cáo khoa học: "Closing the Gap: Learning-Based Information Extraction Rivaling Knowledge-Engineering Methods"

In this paper, we present a learning approach to the scenario template task of information extraction, where information filling one template could come from multiple sentences. When tested on the MUC4 task, our learning approach achieves accuracy competitive to the best of the MUC-4 systems, which were all built with manually engineered rules. Our analysis reveals that our use of full parsing and state-of-the-art learning algorithms have contributed to the good performance. To our knowledge, this is the first research to have demonstrated that a learning approach to the full-scale information extraction task could achieve performance rivaling that of. | Closing the Gap Learning-Based Information Extraction Rivaling Knowledge-Engineering Methods Hai Leong Chieu DSO National Laboratories 20 Science Park Drive Singapore 118230 chaileon@ Hwee Tou Ng Department of Computer Science National University of Singapore 3 Science Drive 2 Singapore 117543 nght@ Yoong Keok Lee DSO National Laboratories 20 Science Park Drive Singapore 118230 lyoongke@ Abstract In this paper we present a learning approach to the scenario template task of information extraction where information filling one template could come from multiple sentences. When tested on the MUC-4 task our learning approach achieves accuracy competitive to the best of the MUC-4 systems which were all built with manually engineered rules. Our analysis reveals that our use of full parsing and state-of-the-art learning algorithms have contributed to the good performance. To our knowledge this is the first research to have demonstrated that a learning approach to the full-scale information extraction task could achieve performance rivaling that of the knowledgeengineering approach. 1 Introduction The explosive growth of online texts written in natural language has prompted much research into information extraction IE the task of automatically extracting specific information items of interest from natural language texts. The extracted information is used to fill database records also known as templates in the IE literature. Research efforts on IE tackle a variety of tasks. They include extracting information from semistructured texts such as seminar announcements rental and job advertisements etc. as well as from free texts such as newspaper articles Soderland 1999 . IE from semi-structured texts is easier than from free texts since the layout and format of a semi-structured text provide additional useful clues AYACUCHO 19 JAN 89 - TODAY TWO PEOPLE WERE WOUNDED WHEN A BOMB EXPLODED IN SAN JUAN BAUTISTA MUNICIPALITY OFFICIALS SAID THAT .

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