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
Internet-scale collection of human-reviewed data
Proceedings of the 16th international conference on World Wide Web
A probabilistic graphical model for joint answer ranking in question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Fine-Grained named entity recognition using conditional random fields for question answering
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
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Nowadays, we are faced with finding "trustworthy" answers not only "relevant" answers. This paper proposes a QA model based on answer trustworthiness. Contrary to the past researches which focused simple trust factors of a document, we identified three different answer trustworthiness factors: 1) incorporating document quality at the document layer; 2) representing the authority and reputation of answer sources at the answer source layer; 3) verifying the answers by consulting various QA systems at the sub-QAs layer. In our experiments, the proposed method using all answer trustworthiness factors shows improvement: 237% (0.150 to 0.506 MRR) for answering effectiveness and 92% (28,993 to 2,293 min.) for indexing efficiency.