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.File in questo prodotto:
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