Structure and content analysis for html medical articles: a hidden markov model approach

Authors:
Jie Zou;Daniel Le;George R. Thoma
Affiliations:
Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD;Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD;Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, MD
Venue:
Proceedings of the 2007 ACM symposium on Document engineering
Year:
2007

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Abstract

We describe ongoing research on segmenting and labeling HTML medical journal articles. In contrast to existing approaches in which HTML tags usually serve as strong indicators, we seek to minimize dependence on HTML tags. Designing logical component models for general Web pages is a challenging task. However, in the narrow domain of online journal articles, we show that the HTML document, modeled with a Hidden Markov Model, can be accurately segmented into logical zones.