Higher-order smoothing: a novel semantic smoothing method for text classification
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KünyePOYRAZ, M., KİLİMCİ, Z.H., GANİZ, M.C. (2014). Higher-order smoothing: a novel semantic smoothing method for text classification. Journal of Computer Science and Technology, 29 (3), pp. 376-391. http://dx.doi.org/10.1007/s11390-014-1437-6.
It is known that latent semantic indexing (LSI) takes advantage of implicit higher-order (or latent) structure in the association of terms and documents. Higher-order relations in LSI capture "latent semantics". These findings have inspired a novel Bayesian framework for classification named Higher-Order Naive Bayes (HONB), which was introduced previously, that can explicitly make use of these higher-order relations. In this paper, we present a novel semantic smoothing method named Higher-Order Smoothing (HOS) for the Naive Bayes algorithm. HOS is built on a similar graph based data representation of the HONB which allows semantics in higher-order paths to be exploited. We take the concept one step further in HOS and exploit the relationships between instances of different classes. As a result, we move beyond not only instance boundaries, but also class boundaries to exploit the latent information in higher-order paths. This approach improves the parameter estimation when dealing with insufficient labeled data. Results of our extensive experiments demonstrate the value of HOS on several benchmark datasets.