The role of lexico-semantic feedback in open-domain textual question-answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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Most of existing open domain question answering systems predefine the conceptual category to which answers can belong. So, they cannot generate appropriate answers in every case or must use a strategy that handles exceptions when the concept requested in the question is not prepared in the system. In this paper, we suggest a flexible strategy that can generate the candidate answers which correspond to any nominal target concepts. The proposed question answering system is equipped with general patterns that can extract hyponyms of the nominal target concept with their confidence scores. Therefore, it can create a set of candidate answers from the dynamically generated ontology when a user requests any nominal concept.