UCAM-CORE: Incorporating structured distributional similarity into STS
Tamara Polajnar, Laura Rimell, Douwe Kiela · 2013
This paper describes methods that were sub-mitted as part of the *SEM shared task on Semantic Textual Similarity. Multiple kernels provide different views of syntactic structure, from both tree and dependency parses. The kernels are then combined with simple lex-ical features using Gaussian process regres-sion, which is trained on different subsets of training data for each run. We found that the simplest combination has the highest consis-tency across the different data sets, while in-troduction of more training data and models requires training and test data with matching qualities. 1