Bottom-up learning of logic programs for information extraction from hypertext documents

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Abstract

We present an inductive logic programming bottom-up learning algorithm (BFOIL) for synthesizing logic programs for multi-slot information extraction from hypertext documents. BFOIL learns from positive examples only and uses a logical representation for hypertext documents based on the document object model (DOM). We briefly discuss several BFOIL refinements and show very promising results of our IE system LIPX in comparison to state of the art IE systems.

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CITATION STYLE

APA

Thomas, B. (2003). Bottom-up learning of logic programs for information extraction from hypertext documents. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2838, pp. 435–446). Springer Verlag. https://doi.org/10.1007/978-3-540-39804-2_39

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