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- W1523516510 abstract "We propose and study a novel supervised approach to learning statistical semantic relatedness models from subjectively annotated training examples. The proposed semantic model consists of parameterized co-occurrence statistics associated with textual units of a large background knowledge corpus. We present an efficient algorithm for learning such semantic models from a training sample of relatedness preferences. Our method is corpus independent and can essentially rely on any sufficiently large (unstructured) collection of coherent texts. Moreover, the approach facilitates the fitting of semantic models for specific users or groups of users. We present the results of extensive range of experiments from small to large scale, indicating that the proposed method is effective and competitive with the state-of-the-art." @default.
- W1523516510 created "2016-06-24" @default.
- W1523516510 creator A5025669300 @default.
- W1523516510 creator A5056605860 @default.
- W1523516510 date "2013-11-10" @default.
- W1523516510 modified "2023-09-27" @default.
- W1523516510 title "Semantic Sort: A Supervised Approach to Personalized Semantic Relatedness." @default.
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