Deep Neural Networks for Web Page Information Extraction

Abstract : 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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [14 references]  Display  Hide  Download

https://hal.inria.fr/hal-01557648
Contributor : Hal Ifip <>
Submitted on : Thursday, July 6, 2017 - 1:55:42 PM
Last modification on : Friday, December 1, 2017 - 1:16:25 AM
Long-term archiving on : Wednesday, January 24, 2018 - 1:11:40 AM

File

430537_1_En_14_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Tomas Gogar, Ondrej Hubacek, Jan Sedivy. Deep Neural Networks for Web Page Information Extraction. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. pp.154-163, ⟨10.1007/978-3-319-44944-9_14⟩. ⟨hal-01557648⟩

Share

Metrics

Record views

746

Files downloads

479