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

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.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods

Additional Classification:
  I. Computing Methodologies
  I.7 DOCUMENT AND TEXT PROCESSING
      I.7.5 Document Capture
          Subjects: Document analysis

General Terms:
Algorithms, Design, Experimentation, Performance

Keywords:
HTML document labeling, HTML document segmentation, document layout analysis, document object model (DOM), text mining, web information retrieval