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Estimating Reliability of Contextual Evidences in Decision-List Classifiers under Bayesian Learning
Natural Language Processing Pacific Rim Symposium
Classifiers are often required to output not only a classification result but also the probability of the classification. We focus on the decision list classifier which has successfully been applied to a wide variety of NLP tasks. We propose methods based on Bayesian learning to calculate the reliability of contextual evidences in decision lists, which enables decision lists to output theoretically well-founded probabilities. Experimental results obtained using Senseval-1 data set show that ourdblp:conf/nlprs/TsuruokaC01 fatcat:ufgguvlpknchzdet7ywbqllq54