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In crowdsourcing systems, the interests of contributing participants and system stakeholders are often not fully aligned. Participants seek to learn, be entertained, and perform easy tasks, which offer them instant gratification; system stakeholders want users to complete more difficult tasks, which bring higher value to the crowdsourced application. We(More)
Guiding principles for selecting the best crowdsourcing methodology for a given information gathering task remain insufficient. This paper contributes additional experimental evidence and analysis to this problem. Our work focuses on a subset of crowdsourcing problems we term expert tasks—tasks that require specific domain knowledge. We experiment with(More)
We employ universal schema for slot filling and cold start. In universal schema, we allow each surface pattern from raw text, and each type defined in ontology, i.e. TACKBP slots to represent relations. And we use matrix factorization to discover implications among surface patterns and target slots. First, we identify mentions of entities from the whole(More)
Cross-sectional imaging has long been employed to examine swallowing in both the sagittal and axial planes. However, data regarding temporal swallow measures in the upright and supine positions are sparse, and none have employed the MBS impairment profile (MBSImP). We report temporal swallow measures, physiologic variables, and swallow safety of upright and(More)
The purpose of this paper is to begin a conversation about the importance and role of confidence estimation in knowledge bases (KBs). KBs are never perfectly accurate, yet without confidence reporting their users are likely to treat them as if they were, possibly with serious real-world consequences. We define a notion of confidence based on the probability(More)
Large-scale author coreference, the problem of ascribing research papers to real-world authors in bibliographic databases, is critical for mining the scientific community. However, traditional pairwise approaches, which measure coreference similarity between pairs of author mentions, scale poorly to large databases; and streaming approaches, which lack the(More)
SCALING MCMC INFERENCE AND BELIEF PROPAGATION TO LARGE, DENSE GRAPHICAL MODELS MAY 2014 SAMEER SINGH B.E., UNIVERSITY OF DELHI M.Sc., VANDERBILT UNIVERSITY Ph.D., UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Andrew McCallum With the physical constraints of semiconductor-based electronics becoming increasingly limiting in the past decade,(More)