This paper focuses on the improvement of the conceptual structure of FrameNet for the sake of applying this resource to knowledge-intensive NLP tasks requiring reasoning, such as question answering, information extraction etc. Ontological analysis supported by data-driven methods is used for axiomatizing, enriching and cleaning up frame relations. The impact of the achieved axiomatization is investigated on recognizing textual entailment.

Data-driven and ontological analysis of Framenet for natural language reasoning

Vieu L;Oltramari A;Borgo S;
2010

Abstract

This paper focuses on the improvement of the conceptual structure of FrameNet for the sake of applying this resource to knowledge-intensive NLP tasks requiring reasoning, such as question answering, information extraction etc. Ontological analysis supported by data-driven methods is used for axiomatizing, enriching and cleaning up frame relations. The impact of the achieved axiomatization is investigated on recognizing textual entailment.
2010
Istituto di Scienze e Tecnologie della Cognizione - ISTC
2-9517408-6-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/133192
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