Brian Kjersten

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Our team submitted runs for the TREC KBA Cumulative Citation Recommendation task. This task involves labeling over 300 million documents for whether they are relevant and/or central to particular entities already in a database. For this task, we used an SVM classifier that uses unigrams and named entities as binary features. In this paper, we describe our(More)
Recent exploratory efforts in discourse-level language modeling have relied heavily on calculating Pointwise Mutual Information (PMI), which involves significant computation when done over large collections. Prior work has required aggressive pruning or independence assumptions to compute scores on large collections. We show the method of Conditional Random(More)
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