An Experimental Study on Boundary Classification Algorithms for Information Extraction using SVM
José Iria, Neil Ireson, Fabio Ciravegna · 2006
This paper investigates the incorporation of diverse features in boundary classification algorithms for IE using SVM. Our study reveals that the use of rich data resources greatly contributes to the performance of IE systems and it is more likely to explain the differences in performance reported by several systems than the design decisions relative to the learning model. Evaluation of our system shows an improvement over the state-of-art on a standard dataset using the same data resources but a much simpler learning model than the previously best-reported system. 1