Semantic Parsing using Distributional Semantics and Probabilistic Logic
Islam Beltagy, Katrin Erk, Raymond J. Mooney · 2014
We propose a new approach to semantic parsing that is not constrained by a fixed formal ontology and purely logical infer-ence. Instead, we use distributional se-mantics to generate only the relevant part of an on-the-fly ontology. Sentences and the on-the-fly ontology are represented in probabilistic logic. For inference, we use probabilistic logic frameworks like Markov Logic Networks (MLN) and Prob-abilistic Soft Logic (PSL). This seman-tic parsing approach is evaluated on two tasks, Textual Entitlement (RTE) and Tex-tual Similarity (STS), both accomplished using inference in probabilistic logic. Ex-periments show the potential of the ap-proach. 1