AuGEAS: authoritativeness grading, estimation, and sorting Ayman Farahat, Geoffrey Nunberg, and Francine Chen   View Paper (PDF)   Return to Information extraction and text segmentation Abstract When searching for content in in a large heterogeneous document collections like the World Wide Web it is not easy to know which documents provide reliable authoritative information about a subject. The problem is particularly pointed as it concerns content search for "high-value" informational needs such as retrieving medical information, where the cost of error may be high. In this paper, a method is described for estimating the authoritativeness of a document based on textual, non-topical cues. This method is complementary to estimates of authoritativeness based on link structure, such as the PageRank and HITS algorithms. This method is particularly suited to "high-value" content search where the user is interested in searching for information about a specific topic. A method for combining textual estimates of authoritativeness with link analysis is also presented. The types of textual cues to authoritativeness that are easily computed and utilized by our method are described, as well as the method used to select a subset of cues to increase the computation speed. Methods for applying authoritativeness estimates to re-ranking documents returned from search engines, combining textual authoritativeness with social authority, and use in query expansion are also presented. By combining textual authority with link analysis, a more complete and robust estimate can be made of a document's authoritativeness. DiSC'03 © 2003 Association for Computing Machinery