Hierarchical text classification for a large-scale Web taxonomy is challenging because the number of categories hierarchically organized is large and the training data for deep categories are usually sparse. It’s been shown that a narrow-down approach involving a search of the taxonomical tree is an effective method for the problem. A recent study showed that both local and global information for a node is useful for further improvement. This paper introduces two methods for mixing local and global models dynamically for individual nodes and shows they improve classification effectiveness by 5% and 30%, respectively, over and above the state-of-art method.
CITATION STYLE
Oh, H. S., Choi, Y., & Myaeng, S. H. (2011). Text classification for a large-scale taxonomy using dynamically mixed local and global models for a node. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6611 LNCS, pp. 7–18). Springer Verlag. https://doi.org/10.1007/978-3-642-20161-5_4
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