Adapting the Naive Bayes Classifier to Rank Procedural Texts

  • Ling Yin
  • Richard Power
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3936)


This paper presents a machine-learning approach for ranking web documents according to the proportion of procedural text they contain. By ‘procedural text’ we refer to ordered lists of steps, which are very common in some instructional genres such as online manuals. Our initial training corpus is built up by applying some simple heuristics to select documents from a large collection and contains only a few documents with a large proportion of procedural texts. We adapt the Naive Bayes classifier to better fit this less than ideal training corpus. This adapted model is compared with several other classifiers in ranking procedural texts using different sets of features and is shown to perform well when only highly distinctive features are used.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ling Yin
    • 1
  • Richard Power
    • 2
  1. 1.Natural Language Technology Group (NLTG)University of BrightonBrightonUnited Kingdom
  2. 2.Faculty of Mathematics and ComputingThe Open UniversityMilton KeynesUnited Kingdom

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