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
Contributo in atti di convegno
European language resources association (ELRA), Paris, FRA
Seventh conference on International Language Resources and Evaluation (LREC'10), pp. 3157–3164, Valletta Malta, May 17-23, 2010