Papers › Deep Neural Networks for Web Page Information Extraction
Deep Neural Networks for Web Page Information Extraction
Tomas Gogar, Hubacek Ondrej, and Jan Sedivy
Web wrappers are systems for extracting structured information from web pages. Currently, wrappers need to be adapted to a particular website template before they can start the extraction process. In this work we present a new method, which uses convolutional neural networks to learn a wrapper that can extract information from previously unseen templates. Therefore, this wrapper does not need any site-specific initialization and is able to extract information from a single web page. We also propose a method for spatial text encoding, which allows us to encode visual and textual content of a web page into a single neural net. The first experiments with product information extraction showed very promising results and suggest that this approach can lead to a general site-independent web wrapper.
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