从老朋友那里骗取信任：来自转换规则列表的概率分类
Coaxing Confidences from an Old Friend: Probabilistic Classifications from Transformation Rule Lists
摘要：
基于转换的学习已经成功地用于解决许多自然语言处理问题。它有许多积极的特点，但一个缺点是，它不提供类成员概率的估计。在本文中，我们提出了一种新的方法，从基于转换的规则列表分类器获得类成员概率。三个实验测量的建模精度和交叉熵的概率分类器上看不见的数据和程度，从分类器的输出概率可以用来估计其分类决策的置信度。这些实验的结果表明，对于文本组块的任务，由这种技术产生的估计比由最先进的决策树产生的估计信息量更大。
英文摘要：
Transformation-based learning has been successfully employed to solve many natural language processing problems. It has many positive features, but one drawback is that it does not provide estimates of class membership probabilities. In this paper, we present a novel method for obtaining class membership probabilities from a transformation-based rule list classifier. Three experiments are presented which measure the modeling accuracy and cross-entropy of the probabilistic classifier on unseen data and the degree to which the output probabilities from the classifier can be used to estimate confidences in its classification decisions. The results of these experiments show that, for the task of text chunking, the estimates produced by this technique are more informative than those generated by a state-of-the-art decision tree.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
