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

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Date

2012-04-27

Author

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.

Subject Keywords

Information extraction, Test instance, Machine learning technique, Training instance, Extraction rule

URI

https://hdl.handle.net/11511/48316

DOI

https://doi.org/10.1007/978-3-642-28997-2_65

Collections

Department of Computer Engineering, Conference / Seminar

Citation Formats

R. Ozcan, İ. S. Altıngövde, and Ö. Ulusoy, “In praise of laziness: A lazy strategy for web information extraction,” 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/48316.