Probabilistic Soft Logic for Semantic Textual Similarity

Probabilistic Soft Logic (PSL) is a re-cently developed framework for proba-bilistic logic. We use PSL to combine logical and distributional representations of natural-language meaning, where distri-butional information is represented in the form of weighted inference rules. We ap-ply this framework to the task of Seman-tic Textual Similarity (STS) (i.e. judg-ing the semantic similarity of natural-language sentences), and show that PSL gives improved results compared to a pre-vious approach based on Markov Logic Networks (MLNs) and a purely distribu-tional approach. 1