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

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2012-04-27
Ozcan, Rifat
Altıngövde, İsmail Sengör
Ulusoy, Özgür
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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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.
Information extraction, Test instance, Machine learning technique, Training instance, Extraction rule
https://hdl.handle.net/11511/48316
Department of Computer Engineering, Conference / Seminar
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