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Robust Web Data Extraction Based on Unsupervised Visual Validation

  • Benoit PotvinEmail author
  • Roger VillemaireEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11431)

Abstract

Visual validation is the process of validating sets of extracted entities by means of visual information. The main advantage of visual validation is to make use of visual information for web information extraction without impacting on the robustness of extractors. In this paper, we show that unsupervised visual validation can be used to create robust web data extractors. More precisely, we evaluate the performance of visual validation on a corpus of visually heterogeneous documents. The selected extraction task consists in extracting the price, name, description, and SKU of unspecified products from unseen documents. Our corpus contains 1000 various products from 100 different sources, which we render public. Results also show that visual validation improves web data extraction even when the extractor is trained with visual features.

Keywords

Visual validation Robustness Isolation forest Web information extraction Classifiers 

Notes

Acknowledgments

The authors gratefully acknowledge the financial support of the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversité du Québec à MontréalMontréalCanada

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