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This paper studies document ranking under uncertainty. It is tackled in a general situation where the relevance predictions of individual documents have uncertainty, and are dependent between each other. Inspired by the Modern Portfolio Theory, an economic theory dealing with investment in financial markets, we argue that ranking under uncertainty is not(More)
Web users are facing information overload problems, i.e., it is hard for them to find desired information on the web. Hence the growing interest in named entity recognition (NER) for discovering relevant information on users’ behalf. We present a browser plug-in called ESpotter which adapts lexicons and patterns to a domain hierarchy consisting of domains(More)
Over the past decades, significant progress has been made in Information Retrieval (IR), ranging from efficiency and scalability to theoretical modeling and evaluation. However, many grand challenges remain. Recently, more and more attention has been paid to the research in domain specific IR applications, as evidenced by the organization of Genomics and(More)
The large number of Web pages on many Web sites has raised navigational problems. Markov chains have recently been used to model user navigational behavior on the World Wide Web (WWW). In this paper, we propose a method for constructing a Markov model of a Web site based on past visitor behavior. We use the Markov model to make link predictions that assist(More)
TREC 2009 was the first year of the Chemical IR Track, which focuses on evaluation of search techniques for discovery of digitally stored information on chemical patents and academic journal articles. The track included two tasks: Prior Art (PA) and Technical Survey (TS) tasks. This paper describes how we designed the two tasks and presents the official(More)
Most retrieval models estimate the relevance of each document to a query and rank the documents accordingly. However, such an approach ignores the uncertainty associated with the estimates of relevancy. If a high estimate of relevancy also has a high uncertainty, then the document may be very relevant or not relevant at all. Another document may have a(More)
This paper presents a new way of thinking for IR metric optimization. It is argued that the optimal ranking problem should be factorized into two distinct yet interrelated stages: the relevance prediction stage and ranking decision stage. During retrieval the relevance of documents is not known a priori, and the joint probability of relevance is used to(More)
Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. ABSTRACT Expert finding is a key task in enterprise search and has recently attracted lots of attention from both(More)
User traversals on hyperlinks between Web pages can reveal semantic relationships between these pages. We use user traversals on hyperlinks as weights to measure semantic relationships between Web pages. On the basis of these weights, we propose a novel method to put Web pages on a Web site onto different conceptual levels in a link hierarchy. We develop a(More)