A stopping criterion for active learning

@article{citeulike:2207755, abstract = {Active learning (AL) is a framework that attempts to reduce the cost of annotating training material for statistical learning methods. While a lot of papers have been presented on applying AL to natural language processing tasks reporting impressive savings, little work has been done on defining a stopping criterion. In this work, we present a stopping criterion for active learning based on the way instances are selected during uncertainty-based sampling and verify its applicability in a variety of settings. The statistical learning models used in our study are support vector machines (SVMs), maximum entropy models and Bayesian logistic regression and the tasks performed are text classification, named entity recognition and shallow parsing. In addition, we present a method for multiclass mutually exclusive SVM active learning.}, author = {Vlachos, Andreas }, citeulike-article-id = {2207755}, doi = {http://dx.doi.org/10.1016/j.csl.2007.12.001}, journal = {Computer Speech \& Language}, keywords = {active, active\_learning, classification, criterion, entity, learning, named, recognition, stopping, text}, posted-at = {2008-01-08 14:22:53}, priority = {0}, title = {A stopping criterion for active learning}, url = {http://dx.doi.org/10.1016/j.csl.2007.12.001}, volume = {In Press, Uncorrected Proof} }

TY - JOUR ID - citeulike:2207755 L3 - citeulike-article-id:2207755 N2 - Active learning (AL) is a framework that attempts to reduce the cost of annotating training material for statistical learning methods. While a lot of papers have been presented on applying AL to natural language processing tasks reporting impressive savings, little work has been done on defining a stopping criterion. In this work, we present a stopping criterion for active learning based on the way instances are selected during uncertainty-based sampling and verify its applicability in a variety of settings. The statistical learning models used in our study are support vector machines (SVMs), maximum entropy models and Bayesian logistic regression and the tasks performed are text classification, named entity recognition and shallow parsing. In addition, we present a method for multiclass mutually exclusive SVM active learning. JF - Computer Speech & Language TI - A stopping criterion for active learning VL - In Press, Uncorrected Proof KW - active KW - active_learning KW - classification KW - criterion KW - entity KW - learning KW - named KW - recognition KW - stopping KW - text AU - Vlachos, Andreas UR - http://dx.doi.org/10.1016/j.csl.2007.12.001 ER -