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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
A Data-Dependent Distance Measure For Transductive Instance-Based Learning, Jared Lundell, Dan A. Ventura
A Data-Dependent Distance Measure For Transductive Instance-Based Learning, Jared Lundell, Dan A. Ventura
Faculty Publications
We consider learning in a transductive setting using instance-based learning (k-NN) and present a method for constructing a data-dependent distance “metric” using both labeled training data as well as available unlabeled data (that is to be classified by the model). This new data-driven measure of distance is empirically studied in the context of various instance-based models and is shown to reduce error (compared to traditional models) under certain learning conditions. Generalizations and improvements are suggested.
Active Learning For Part-Of-Speech Tagging: Accelerating Corpus Annotation, George Busby, Marc Carmen, James Carroll, Robbie Haertel, Deryle W. Lonsdale, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi
Active Learning For Part-Of-Speech Tagging: Accelerating Corpus Annotation, George Busby, Marc Carmen, James Carroll, Robbie Haertel, Deryle W. Lonsdale, Peter Mcclanahan, Eric K. Ringger, Kevin Seppi
Faculty Publications
In the construction of a part-of-speech annotated corpus, we are constrained by a fixed budget. A fully annotated corpus is required, but we can afford to label only a subset. We train a Maximum Entropy Markov Model tagger from a labeled subset and automatically tag the remainder. This paper addresses the question of where to focus our manual tagging efforts in order to deliver an annotation of highest quality. In this context, we find that active learning is always helpful. We focus on Query by Uncertainty (QBU) and Query by Committee (QBC) and report on experiments with several baselines and …