Abstract
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.
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Oh, HS., Choi, Y., Myaeng, SH. (2011). Text Classification for a Large-Scale Taxonomy Using Dynamically Mixed Local and Global Models for a Node. In: Clough, P., et al. Advances in Information Retrieval. ECIR 2011. Lecture Notes in Computer Science, vol 6611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20161-5_4
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DOI: https://doi.org/10.1007/978-3-642-20161-5_4
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