We propose a new approach to semantic parsing that is not constrained by a fixed formal ontology and purely logical inference. Instead, we use distributional semantics 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 Probabilistic Soft Logic (PSL). This semantic parsing approach is evaluated on two tasks, Textual Entitlement (RTE) and Textual Similarity (STS), both accomplished using inference in probabilistic logic. Experiments show the potential of the approach.

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In Proceedings of ACL 2014 Workshop on Semantic Parsing (SP-2014), pp. 7--11, Baltimore, MD, June 2014.

@inproceedings{beltagy:sp14, title={Semantic Parsing using Distributional Semantics and Probabilistic Logic}, author={I. Beltagy and Katrin Erk and Raymond Mooney}, booktitle={Proceedings of ACL 2014 Workshop on Semantic Parsing (SP-2014)}, month={June}, address={Baltimore, MD}, pages={7--11}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127440", year={2014} }

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