Paper: iKernels-Core: Tree Kernel Learning for Textual Similarity

ACL ID S13-1006
Title iKernels-Core: Tree Kernel Learning for Textual Similarity
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

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 sl...