2013/11/10 by Ran El‐Yaniv, Ran El-Yaniv, El-Yaniv, Ran +2
Computer Science · #Natural Language Processing Techniques #Semantic Web and Ontologies #Topic Modeling #cs.CL #cs.LG
paper · pdf · doi:10.48550/arxiv.1311.2252
37 pages, 8 figures A short version of this paper was already published at ECML/PKDD 2012
arxiv created 2013/11/10 · arxiv updated 2013/11/12
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.