In this paper we introduce the ttcs system, so named after Terms To Conceptual Spaces, that exploits a resource-driven approach relying on BabelNet, NASARI and ConceptNet. ttcs takes in input a term and its context of usage and produces as output a specific type of vector-based semantic representation, where conceptual information is encoded through the Conceptual Spaces (a geometric framework for common-sense knowledge representation and reasoning). The system has been evaluated in a twofold experimentation. In the first case we assessed the quality of the extracted common-sense conceptual information with respect to human judgments with an online questionnaire. In the second one we compared the performances of a conceptual categorization system that was run twice, once fed with extracted annotations and once with hand-crafted annotations. In both cases the results are encouraging and provide precious insights to make substantial improvements.

A Resource-Driven Approach for Anchoring Linguistic Resources to Conceptual Spaces

LIETO, ANTONIO;MENSA, ENRICO;RADICIONI, DANIELE PAOLO
2016-01-01

Abstract

In this paper we introduce the ttcs system, so named after Terms To Conceptual Spaces, that exploits a resource-driven approach relying on BabelNet, NASARI and ConceptNet. ttcs takes in input a term and its context of usage and produces as output a specific type of vector-based semantic representation, where conceptual information is encoded through the Conceptual Spaces (a geometric framework for common-sense knowledge representation and reasoning). The system has been evaluated in a twofold experimentation. In the first case we assessed the quality of the extracted common-sense conceptual information with respect to human judgments with an online questionnaire. In the second one we compared the performances of a conceptual categorization system that was run twice, once fed with extracted annotations and once with hand-crafted annotations. In both cases the results are encouraging and provide precious insights to make substantial improvements.
2016
XVth International Conference of the Italian Association for Artificial Intelligence
Genova, Italy
November 29 – December 1, 2016
Proceedings of the XVth International Conference of the Italian Association for Artificial Intelligence
Springer
10037
435
449
978-3-319-49129-5
http://link.springer.com/chapter/10.1007/978-3-319-49130-1_32
Lieto, Antonio; Mensa, Enrico; Radicioni, Daniele P.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1630845
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