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

Ozcan, Rifat
Altıngövde, İsmail Sengör
Ulusoy, Özgür
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.