Multi-label Text Categorization Using K-Nearest Neighbor Approach with M-Similarity

  • Yi Feng
  • Zhaohui Wu
  • Zhongmei Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3772)

Abstract

Due to the ubiquity of textual information nowadays and the multi-topic nature of text, it is of great necessity to explore multi-label text categorization problem. Traditional methods based on vector-space-model text representation suffer the losing of word order information. In this paper, texts are considered as symbol sequences. A multi-label lazy learning approach named kNN-M is proposed, which is derived from traditional k-nearest neighbor (kNN) method. The flexible order-semisensitive measure, M-Similarity, which enables the usage of sequence information in text by swap-allowed dynamic block matching, is applied to evaluate the closeness of texts on finding k-nearest neighbors in kNN-M. Experiments on real-world OHSUMED datasets illustrate that our approach outperforms existing ones considerably, showing the power of considering both term co-occurrence and order on text categorization tasks.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yi Feng
    • 1
  • Zhaohui Wu
    • 1
  • Zhongmei Zhou
    • 1
  1. 1.College of Computer ScienceZhejiang UniversityHangzhouP.R. China

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