  Determining semantic textual similarity is a core research subject in natural
language processing. Since vector-based models for sentence representation
often use shallow information, capturing accurate semantics is difficult. By
contrast, logical semantic representations capture deeper levels of sentence
semantics, but their symbolic nature does not offer graded notions of textual
similarity. We propose a method for determining semantic textual similarity by
combining shallow features with features extracted from natural deduction
proofs of bidirectional entailment relations between sentence pairs. For the
natural deduction proofs, we use ccg2lambda, a higher-order automatic inference
system, which converts Combinatory Categorial Grammar (CCG) derivation trees
into semantic representations and conducts natural deduction proofs.
Experiments show that our system was able to outperform other logic-based
systems and that features derived from the proofs are effective for learning
textual similarity.
