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.
This research is supported by National Basic Research Priorities Programme of China Ministry of Science and Technology (2004DKA20250) and China 973 project (2003CB317006).
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Amir, A., Aumann, Y., Landau, G.M., Lewenstein, M., Lewenstein, N.: Pattern Matching with Swaps. Journal of Algorithms 37, 247–266 (2000)
Feng, Y., Wu, Z., Zhou, Z.: Combining an Order-semisensitive Text Similarity and Closest Fit Approach to Textual Missing Values in Knowledge Discovery. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3682, pp. 943–949. Springer, Heidelberg (2005)
Lodhi, H., Saunders, C., et al.: Text Classification using String Kernels. Journal of Machine Learning Research 2, 419–444 (2002)
McCallum, A.K.: Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering (1996), http://www-2.cs.cmu.edu/~mccallum/bow
Schapire, R.E., Singer, Y.: BoosTexter: A boosting-based system for text categorization. Machine Learning 39(2/3), 135–168 (2000)
Tichy, W.F.: The string to string correction problem with block moves. ACM Trans. Comp. Sys. 2(4), 309–321 (1984)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Feng, Y., Wu, Z., Zhou, Z. (2005). Multi-label Text Categorization Using K-Nearest Neighbor Approach with M-Similarity. In: Consens, M., Navarro, G. (eds) String Processing and Information Retrieval. SPIRE 2005. Lecture Notes in Computer Science, vol 3772. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11575832_17
Download citation
DOI: https://doi.org/10.1007/11575832_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29740-6
Online ISBN: 978-3-540-32241-2
eBook Packages: Computer ScienceComputer Science (R0)