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ABSTRACT
In this paper we present the features of a Question/Answering (Q/A) system that had unparalleled performance in the TREC-9 evaluations. We explain the accuracy of our system through the unique characteristics of its architecture: (1) usage of a wide-coverage answer type taxonomy; (2) repeated passage retrieval; (3) lexico-semantic feedback loops; (4) extraction of the answers based on machine learning techniques; and (5) answer caching. Experimental results show the effects of each feature on the overall performance of the Q/A system and lead to general conclusions about Q/A from large text collections.
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CITED BY 30
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Susan Dumais , Michele Banko , Eric Brill , Jimmy Lin , Andrew Ng, Web question answering: is more always better?, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland
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Constantin Orǎsan , Doina Tatar , Gabriela Şerban , Dana Lupsa , Adrian Onet, How to build a QA system in your back-garden: application for Romanian, Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics, April 12-17, 2003, Budapest, Hungary
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Wei Li , Rohini K. Srihari , Xiaoge Li , M. Srikanth , Xiuhong Zhang , Cheng Niu, Extracting exact answers to questions based on structural links, proceeding of the 2002 conference on multilingual summarization and question answering, p.1-9, August 31, 2002
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Johan Bos , Stephen Clark , Mark Steedman , James R. Curran , Julia Hockenmaier, Wide-coverage semantic representations from a CCG parser, Proceedings of the 20th international conference on Computational Linguistics, p.1240-es, August 23-27, 2004, Geneva, Switzerland
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Jinwen Guo , Shengliang Xu , Shenghua Bao , Yong Yu, Tapping on the potential of q&a community by recommending answer providers, Proceeding of the 17th ACM conference on Information and knowledge management, October 26-30, 2008, Napa Valley, California, USA
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