Abstract
Most of traditional text clustering methods are based on bag of words representation, which ignore the important information on semantic relationship between key terms. To overcome this problem, researchers have recently proposed several new methods for improving short text clustering accuracy based on enriching short text representation. However, the computational costs of these methods based on expanding words appeared in short texts are usually time-consuming. In this paper, we improve previous work by enriching short text representation with keyword expansion. Empirical results show that the proposed method can greatly save time without sacrificing clustering accuracy.
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Wang, J., Zhou, Y., Li, L., Hu, B., Hu, X. (2009). Improving Short Text Clustering Performance with Keyword Expansion. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_31
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DOI: https://doi.org/10.1007/978-3-642-01216-7_31
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