iKernels-Core: Tree Kernel Learning for Textual Similarity

This paper describes the participation of iKernels system in the Semantic Textual Similarity (STS) shared task at *SEM 2013. Different from the majority of approaches, where a large number of pairwise similarity features are used to learn a regression model, our model directly encodes the input texts into syntactic/semantic structures. Our systems rely on tree kernels to automatically extract a rich set of syntactic patterns to learn a similarity score correlated with human judgements. We experiment with different structural representations derived from constituency and dependency trees. While showing large improvements over the top results from the previous year task (STS-2012), our best system ranks 21st out of total 88 participated in the STS2013 task. Nevertheless, a slight refinement to our model makes it rank 4th.