Stylistic and lexical co-training for web block classification

ABSTRACT

Many applications which use web data extract information from a limited number of regions on a web page. As such, web page division into blocks and the subsequent block classification have become a preprocessing step. We introduce PARCELS, an open-source, co-trained approach that performs classification based on separate stylistic and lexical views of the web page. Unlike previous work, PARCELS performs classification on fine-grained blocks. In addition to table-based layout, the system handles real-world pages which feature layout based on divisions and spans as well as stylistic inference for pages using cascaded style sheets. Our evaluation shows that the co-training process results in a reduction of 28.5% in error rate over a single-view classifier and that our approach is comparable to other state-of-the-art systems.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.7 DOCUMENT AND TEXT PROCESSING
      I.7.m Miscellaneous

Additional Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.4 Hypertext/Hypermedia

General Terms:
Algorithms, Experimentation

Keywords:
PARCELS, co-training, lexical and stylistic learners, web page block classification, web page division