Web Development

Towards Combining Web Classification and Web Information Extraction: A Case Study

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Executive Summary

Web content analysis often has two sequential and separate steps: Web Classification to identify the target Web pages, and Web Information Extraction to extract the metadata contained in the target Web pages. This decoupled strategy is highly ineffective since the errors in Web classification will be propagated to Web information extraction and eventually accumulate to a high level. This paper studies the mutual dependencies between these two steps and proposes to combine them by using a model of Conditional Random Fields (CRFs). This model can be used to simultaneously recognize the target Web pages and extract the corresponding metadata. Systematic experiments in the project Of-Course for online course search show that this model significantly improves the F1 value for both of the two steps.

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