Abstract
This paper presents a semantic logic-based approach to determine textual similarity. Three logic form transformations taking into account semantic structure of sentences are proposed. Logic proofs are obtained using an adapted resolution step that drops predicates when a proof cannot be found with standard resolution. Features are extracted from proofs and combined using supervised machine learning to obtain the final similarity scores. Experimental results show that taking into account semantic relations to determine textual similarity yields performance improvements with respect to both baselines and third-party state-of-the-art systems. Specific sentence pairs that benefit from considering semantic relations are discussed. Detailed results provide empirical evidence that either proof direction offers a strong baseline although considering both is beneficial, and that ignoring concepts that are not an argument of a semantic relation is not sound.
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