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            <title>Proceedings of the Workshop on Computational Semantics beyond Events and Roles</title>
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            <namePart type="given">Eduardo</namePart>
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    <abstract>This paper describes the first version of an open-source semantic parser that creates graphical representations of sentences to be used for further semantic processing, e.g. for natural language inference, reasoning and semantic similarity. The Graphical Knowledge Representation which is output by the parser is inspired by the Abstract Knowledge Representation, which separates out conceptual and contextual levels of representation that deal respectively with the subject matter of a sentence and its existential commitments. Our representation is a layered graph with each sub-graph holding different kinds of information, including one sub-graph for concepts and one for contexts. Our first evaluation of the system shows an F-score of 85% in accurately representing sentences as semantic graphs.</abstract>
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%0 Conference Proceedings
%T GKR: the Graphical Knowledge Representation for semantic parsing
%A Kalouli, Aikaterini-Lida
%A Crouch, Richard
%Y Blanco, Eduardo
%Y Morante, Roser
%S Proceedings of the Workshop on Computational Semantics beyond Events and Roles
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F kalouli-crouch-2018-gkr
%X This paper describes the first version of an open-source semantic parser that creates graphical representations of sentences to be used for further semantic processing, e.g. for natural language inference, reasoning and semantic similarity. The Graphical Knowledge Representation which is output by the parser is inspired by the Abstract Knowledge Representation, which separates out conceptual and contextual levels of representation that deal respectively with the subject matter of a sentence and its existential commitments. Our representation is a layered graph with each sub-graph holding different kinds of information, including one sub-graph for concepts and one for contexts. Our first evaluation of the system shows an F-score of 85% in accurately representing sentences as semantic graphs.
%R 10.18653/v1/W18-1304
%U https://aclanthology.org/W18-1304
%U https://doi.org/10.18653/v1/W18-1304
%P 27-37

Markdown (Informal)

[GKR: the Graphical Knowledge Representation for semantic parsing](https://aclanthology.org/W18-1304) (Kalouli & Crouch, SemBEaR 2018)

• GKR: the Graphical Knowledge Representation for semantic parsing (Kalouli & Crouch, SemBEaR 2018)

ACL

• Aikaterini-Lida Kalouli and Richard Crouch. 2018. GKR: the Graphical Knowledge Representation for semantic parsing. In Proceedings of the Workshop on Computational Semantics beyond Events and Roles, pages 27–37, New Orleans, Louisiana. Association for Computational Linguistics.