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With the prevalence of the Web and social media, users increasingly express their preferences online. In learning these preferences, recommender systems need to balance the trade-off between exploitation, by providing users with more of the "same", and exploration, by providing users with something "new" so as to expand the systems' knowledge. Multi-armed… (More)
We present two methods for entity linking in two of our systems submitted to TAC-KBP 2012. The first one, implemented in JVN-TDT1 system, learns coherence among co-occurrence entities referred to within a text by exploiting Wikipedia's link structure and the second one, implemented in JVN_TDT2 system , combines some heuristics with a statistical model, for… (More)
Hantavirus pulmonary syndrome (HPS) is a rare illness in eastern Canada. We present three cases of HPS among military personnel in Quebec. The three cases shared a common exposure to mouse excreta while engaged in military training in Alberta, a western province of Canada.
Ambiguity in meanings of words or phrases in a document is considered one of the most primary barriers in natural language processing. In this work, we address the task of identifying and linking mentions of entities into correct referents described in a given knowledge base. To deal with it, we propose a supervised learning method for ranking candidate… (More)
Users express their personal preferences through ratings, adoptions, and other consumption behaviors. We seek to learn latent representations for user preferences from such behavioral data. One representation learning model that has been shown to be effective for large preference datasets is Restricted Boltzmann Machine (RBM). While homophily, or the… (More)