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Communities & Collections
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In praise of laziness: A lazy strategy for web information extraction
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index.pdf
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