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In this paper, we describe our submission to the Shared Task #1. We tried to follow the underlying idea of the task, that is, evaluating the gap of full-fledged recognizing textual entailment systems with respect to compositional distributional semantic models (CDSMs) applied to this task. We thus submitted two runs: 1) a system obtained with a machine learning approach based on the feature spaces of rules with variables and 2) a system completely based on a CDSM that mixes structural and syntactic information by using distributed tree kernels. Our analysis shows that, under the same conditions, the fully CDSM system is still far from being competitive with more complex methods.

haLF: Comparing a Pure CDSM Approach with a Standard Machine Learning System for RTE