Abstract
A new text categorization algorithm based on Hidden Markov Model is proposed. At first, semantic clusters are obtained from training data set. The association between semantic clusters is modeled as Hidden Markov Model. Combining with the forward algorithm, the strategy could realize automatic text categorization. From the simulation, the proposed text categorization algorithm is better in categorization precision. Moreover, it works well independent of the number of considered categories compared to the priori art algorithms.
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Li, F., Dong, T. (2013). Text Categorization Based on Semantic Cluster-Hidden Markov Models. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_24
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DOI: https://doi.org/10.1007/978-3-642-38715-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38714-2
Online ISBN: 978-3-642-38715-9
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