Data-Driven and Ontological Analysis of FrameNet for Natural Language Reasoning

This paper focuses on the improvement of the conceptual structure of FrameNet for the sake of applying this resource to knowledgeintensive 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.

• Type: Conference
• Year: 2010
• Where: LREC
• Authors: Ekaterina Ovchinnikova, Laure Vieu, Alessandro Oltramari, Stefano Borgo, Theodore Alexandrov
• Keywords: Education, Information Extraction Etc, LREC 2010, Tasks Requiring Reasoning, Textual Entailment