In praise of laziness: A lazy strategy for web information extraction

Abstract

A large number of Web information extraction algorithms are based on machine learning techniques. For such extraction algorithms, we propose employing a lazy learning strategy to build a specialized model for each test instance to improve the extraction accuracy and avoid the disadvantages of constructing a single general model. © 2012 Springer-Verlag Berlin Heidelberg.