CLaC-CORE, an exhaustive feature combina- tion system ranked 4th among 34 teams in the Semantic Textual Similarity shared task STS 2013. Using a core set of 11 lexical features of the most basic kind, it uses a support vector regressor which uses a combination of these lexical features to train a model for predicting similarity between sentences in a two phase method, which in turn uses all combinations of the features in the feature space and trains separate models based on each combination. Then it creates a meta-feature space and trains a final model based on that. This two step pro- cess improves the results achieved by single- layer standard learning methodology over the same simple features. We analyze the correla- tion of feature combinations with the data sets over which they are e...

Click Here to View the Paper In XML Format