Abstract
How can we distinguish accurate information from inaccurate or untrustworthy information is a big challenge in the field of information retrieval. This paper discusses trust as a factoid learning problem, which extracts factoid from the content and then rank them according to their likehood as trustworthy ones. Learning methods for performing factoid ranking are proposed in this paper, and then we combine our method with the famous PageRank algorithm to form a more powerful method for retrieval reliable information. Evaluating of the model and the experimental results were presented. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Ni, Y., & Wang, W. (2007). A novel factoid ranking model for information retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4537 LNCS, pp. 308–316). Springer Verlag. https://doi.org/10.1007/978-3-540-72909-9_35
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