Dealing with semantic representations of concepts involves collecting information on many aspects that collectively contribute to (lexical, semantic and ultimately) linguistic competence. In the last few years mounting experimental evidences have been gathered in the fields of Neuroscience and Cognitive Science on conceptual access and retrieval dynamics that posit novel issues, such as the imageability associated to terms and concepts, or abstractness features as a correlate of figurative uses of language. However, this body of research has not yet penetrated Computational Linguistics: specifically, as regards as Lexical Semantics, in the last few years the field has been dominated by distributional models and vectorial representations. We recently proposed COVER, that relies on a partly different approach. Conceptual descriptions herein are aimed at putting together the lexicographic precision of BabelNet and the common-sense available in ConceptNet. We now propose Abs-COVER, that extends the existing lexical resource by associating an abstractness score to the concepts contained therein. We introduce the detailed algorithms and report about an extensive evaluation on the renewed resource, where we obtained correlations with human judgements in line or higher compared to state of the art approaches.

Annotating Concept Abstractness by Common-sense Knowledge

Enrico Mensa;PORPORATO, AURELIANO;Daniele P. Radicioni
2018

Abstract

Dealing with semantic representations of concepts involves collecting information on many aspects that collectively contribute to (lexical, semantic and ultimately) linguistic competence. In the last few years mounting experimental evidences have been gathered in the fields of Neuroscience and Cognitive Science on conceptual access and retrieval dynamics that posit novel issues, such as the imageability associated to terms and concepts, or abstractness features as a correlate of figurative uses of language. However, this body of research has not yet penetrated Computational Linguistics: specifically, as regards as Lexical Semantics, in the last few years the field has been dominated by distributional models and vectorial representations. We recently proposed COVER, that relies on a partly different approach. Conceptual descriptions herein are aimed at putting together the lexicographic precision of BabelNet and the common-sense available in ConceptNet. We now propose Abs-COVER, that extends the existing lexical resource by associating an abstractness score to the concepts contained therein. We introduce the detailed algorithms and report about an extensive evaluation on the renewed resource, where we obtained correlations with human judgements in line or higher compared to state of the art approaches.
The 17th International Conference of the Italian Association for Artificial Intelligence - AI*IA 2018 - Advances in Artificial Intelligence
Trento, ITALY
20-23 novembre 2018
Proceedings of the 17th International Conference of the Italian Association for Artificial Intelligence`
Springer International Publishing
11298
415
428
978-3-030-03839-7
https://link.springer.com/chapter/10.1007/978-3-030-03840-3_31
Enrico Mensa, Aureliano Porporato, Daniele P. Radicioni
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1685415
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