J. Kandola, J. Shawe-Taylor, and N. Cristianini. 2002. Learning semantic similarity. In NIPS'02) - MIT Press. Home/Search Document Details and Download
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.... A similar idea has also been investigated more recently under the title of semantic keaels and utilized in conjunc tion with SVMs for text categorization [14] Yet another approach of deriving document representations that takes semantic similarities of terms into account has been pro posed in [15]. In [16] a method to systenmtically derive se mantic representation from pLSA models using the method of Fisher kernels [17] has been presented. The resulting se mantic kernels are combined with a standard vector space representation using a heuristic weighting scheme. Finally, there are ....
....se mantic kernels are combined with a standard vector space representation using a heuristic weighting scheme. Finally, there are related methods that perform word clustering in order to extract more reliable features. This has been inves tigated in the context of naive Bayes classification [15] and more recently in the context of SVMs [19] The main advantage of the proposed method compared to previous work is that it allows us to retain the term based representation and augment rather than to replace it with a semantic representation. For certain categories, individual terms might ....
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J. Kandola, N. Cristianini, and J. Shawe-Taylor. Learning semantic similarity. In Advances in Neural Infonation Processing Systems (to appear), volume 15, 2003.
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J. Kandola, J. Shawe-Taylor, and N. Cristianini. 2002. Learning semantic similarity. In NIPS'02) - MIT Press.
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J. Kandola, J. Shawe-Taylor, and N. Cristianini. Learning semantic similarity. In NIPS, 2002.
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