Estimating Reliability of Contextual Evidences in Decision-List Classifiers under Bayesian Learning

Yoshimasa Tsuruoka, Takashi Chikayama
2001 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 our
more » ... methods enable decision lists to output probabilities appropriately reflecting their reliabilities and improve the classification performance of the decision list algorithm.
dblp:conf/nlprs/TsuruokaC01 fatcat:ufgguvlpknchzdet7ywbqllq54