FALCON: Boosting Knowledge for Answer Engines

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This paper discusses FALCON, an answer engine that integrates different forms of syntactic, semantic and pragmatic knowledge for the goal of achieving better performance.

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10 p.

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Harabagiu, Sanda M.; Moldovan, Dan I.; Paşca, Marius. 1974-; Mihalcea, Rada, 1974-; Surdeanu, Mihai; Bunescu, Răzvan et al. November 2000.

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This paper discusses FALCON, an answer engine that integrates different forms of syntactic, semantic and pragmatic knowledge for the goal of achieving better performance.

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10 p.

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Abstract: This paper presents the features of FALCON, an answer engine that integrates different forms of syntactic, semantic and pragmatic knowledge for the goal of achieving better performance. The answer engine handles question reformulations, finds the expected answer type from a large hierarchy that incorporates the WordNet semantic net and extracts answers after performing unifications on the semantic forms of the question and its candidate answers. To rule out erroneous answers, it provides justification option, implemented as an abductive proof. In TREC-9, FALCON generated a score of 58% for short answers and 76% for long answers.

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  • Ninth Annual Text Retrieval Conference (TREC), 2000, Gaithersburg, Maryland, United States

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UNT Scholarly Works

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  • November 2000

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  • April 13, 2012, 9:48 a.m.

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  • March 27, 2014, 11:58 a.m.

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Harabagiu, Sanda M.; Moldovan, Dan I.; Paşca, Marius. 1974-; Mihalcea, Rada, 1974-; Surdeanu, Mihai; Bunescu, Răzvan et al. FALCON: Boosting Knowledge for Answer Engines, paper, November 2000; (digital.library.unt.edu/ark:/67531/metadc83296/: accessed July 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.