Semantic Sort: A Supervised Approach to Personalized Semantic Relatedness
This work addresses the need for personalized semantic models in natural language processing, though it appears incremental as it builds on existing supervised learning and co-occurrence statistics.
The authors tackled the problem of learning personalized semantic relatedness models from subjectively annotated training examples, and their method proved effective and competitive with state-of-the-art approaches in experiments ranging from small to large scale.
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.