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In this paper, we approach the problem of real-time filtering in the Twitter Microblogging platform. We adapt an effective traditional news filtering technique, which uses a text classifier inspired by Rocchio's relevance feedback algorithm, to build and dynamically update a profile of the user's interests in real-time. In our adaptation, we tackle two(More)
(2012). Automatically structuring domain knowledge from text: a review of current research. 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 This paper(More)
Local search is increasingly attracting more demand, whereby the users are interested to find out about places or events in their local vicinity. In this paper, we propose to use the Twitter microblogging platform to detect and rank local events of interest in real-time. We present a novel event retrieval framework, where both the contents of the tweets and(More)
Search applications have become very popular over the last two decades, one of the main drivers being the advent of the Web. Nevertheless, searching on the Web is very different to searching on smaller, often more structured collections such as digital libraries, local Web sites, and intranets. One way of helping the searcher locating the right information(More)
(2011). AutoEval: an evaluation methodology for evaluating query suggestions using query logs. 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. User(More)
The emergence of crowdsourcing as a commonly used approach to collect vast quantities of human assessments on a variety of tasks represents nothing less than a paradigm shift. This is particularly true in academic research where it has suddenly become possible to collect (high-quality) annotations rapidly without the need of an expert. In this paper we(More)
In TREC 2014, we focus on tackling the challenges posed by the Contextual Suggestion and Temporal Summarisa-tion tracks, as well as enhancing our existing technologies to tackle risk-sensitivity as part of the Web track, building upon our Terrier Information Retrieval Platform. In particular , for the Contextual Suggestion track, we propose a novel bundled(More)