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Volume 24 Issue 4
July  2009
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Hang Guo, Li-Zhu Zhou, Ling Feng. Self-Switching Classification Framework for Titled Documents[J]. Journal of Computer Science and Technology, 2009, 24(4): 615-625.
Citation: Hang Guo, Li-Zhu Zhou, Ling Feng. Self-Switching Classification Framework for Titled Documents[J]. Journal of Computer Science and Technology, 2009, 24(4): 615-625.
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Self-Switching Classification Framework for Titled Documents

  • 1EMC Research China, Tsinghua Science Park, Beijing 100084, China
    2Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Funds: This work was done when the first author was studying in Tsinghua University, China. It is supported by the National Natural Science Foundation of China under Grant Nos. 60833003 and 60773156.
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  • Author Bio:

    Hang Guo is a research scientist at EMC Research China. Hereceived hisPh.D. degree from the Department of Computer Science and Technology, TsinghuaUniversity in 2008. His research interests include text mining, machinelearning and information retrieval.

    Li-Zhu Zhou is a professor at the Department of Computer Scienceand Technology, Tsinghua University. He is a member of ACM. His current research interestsinclude database, digital resource management, and data mining.

    Ling Feng is a professor at the Department of Computer Science andTechnology, Tsinghua University. She is a member of CCF, IEEE and ACM.Her research interests includecontext-aware data management towards ambient intelligence,knowledge-based information systems, data mining and warehousing, anddistributed object-oriented database management systems.

  • Revised Date: February 25, 2009
  • Published Date: July 04, 2009
    Abstract
  • Ambiguous words refer to words that have multiple meanings such as apple, window. In text classification they are usually removed by feature reduction methods like Information Gain. Sometimes there are too many ambiguous words in the corpus, which makes throwing away all of them not a viable option, as in the case when classifying documents from the Web. In this paper we look for a method to classify Titled documents with the help of ambiguous words. Titled documents are a kind of documents that have a simple structure containing a title and an excerpt. News, messages, and paper abstracts with titles are examples of titled documents. Instead of introducing another feature reduction method, we describe a framework to make the best use of ambiguous words in the titled documents. The framework improves the performance of a traditional bag-of-words classifier with the help of a bag-of-word-pairs classifier. The framework is implemented using one of the most popular classifiers, Multinomial NaiveBayes (MNB) as an example. The experiments with three real life datasets show that in our framework the MNB model performs much better than traditional MNB classifier and a naive weighted algorithm, which simply puts more weight on words in the title.
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    • References (17)
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