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SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
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SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
New Directions in Question Answering
New Directions in Question Answering
A framework to predict the quality of answers with non-textual features
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Multi-perspective question answering using the OpQA corpus
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Supporting Judgment of Fact Trustworthiness Considering Temporal and Sentimental Aspects
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
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Proceedings of the Second ACM International Conference on Web Search and Data Mining
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AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
A framework for corroborating answers from multiple web sources
Information Systems
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Information Processing and Management: an International Journal
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In the recent explosion of Web information, it is important to find not only appropriate, but also more trustworthy answers to user questions. This paper proposes an improved ranking model for question answering (QA) which is focused on various answer trustworthiness factors. Contrary to past research that simply focused on document quality, we have identified three different answer trustworthiness factors in multiple layers of answering processes: document quality, authority and reputation of answer sources, and appropriateness of answering method for a given question. Each of these factors is used in the answer selection as an input to the ranking scheme that can be tuned for the confidence value for a particular answer candidate. Through several experiments, we analysed the efficacy of our QA model from two points of view: indexing and answering. In indexing, distilling unreliable documents brings not only a 96% reduction in document size but also a 92% speed increase in indexing time. To reveal the effect of trustworthiness factors in answering, we conducted several experiments to determine the optimum combination of weights of sub-features for trustworthiness factors. Finally, the proposed method using all answer trustworthiness factors obtained an improvement in effectiveness over the simple routing QA by 150% in Top1. We also investigated improvement impacts according to answer trustworthiness factors.