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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Identifying Content Blocks from Web Documents
verfasst von : Sandip Debnath, Prasenjit Mitra, C. Lee Giles
Erschienen in: Foundations of Intelligent Systems
Verlag: Springer Berlin Heidelberg
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Intelligent information processing systems, such as digital libraries or search engines index web-pages according to their informative content. However, web-pages contain several non-informative contents, e.g., navigation sidebars, advertisements, copyright notices, etc. It is very important to separate the informative “primary content blocks” from these non-informative blocks. In this paper, two algorithms,
FeatureExtractor
and
K-FeatureExtractor
are proposed to identify the “primary content blocks” based on their features. None of these algorithms require any supervised learning, but still can identify the “primary content blocks” with high precision and recall. While operating on several thousand web-pages obtained from 15 different websites, our algorithms significantly outperform the Entropy-based algorithm proposed by Lin and Ho [14] in both precision and run-time.