iKernels-Core: Tree Kernel Learning for Textual Similarity
Aliaksei Severyn, Massimo Nicosia, Alessandro Moschitti · 2013
This paper describes the participation of iKer-nels system in the Semantic Textual Similar-ity (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 syntac-tic/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 ex-periment with different structural representa-tions derived from constituency and depen-dency trees. While showing large improve-ments over the top results from the previous year task (STS-2012), our best system ranks 21st out of total 88 participated in the STS-2013 task. Nevertheless, a slight refinement to our model makes it rank 4th. 1