条件随机场中的多准则主动学习

Christopher T. Symons, N. Samatova, R. Krishnamurthy, Byung-Hoon Park, Tarik Umar, David J. Buttler, T. Critchlow, D. Hysom
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引用次数: 23

摘要

条件随机场(crf)是许多自然语言处理(NLP)任务中流行的监督学习模型,通常需要大量标记数据进行训练。但是,在实践中,手工注释文本文档的成本非常高。此外,如果不小心选择,即使是大型标记训练集也可能有任意有限的性能峰值。本文考虑使用多准则主动学习来识别一个小而足够的文本样本集来训练crf。我们的实证结果表明,我们的方法能够降低人工标注成本,同时也限制了通常与主动学习相关的再培训成本。此外,我们还证明了通过明智地选择训练样本可以提高crf的泛化性能
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Criterion Active Learning in Conditional Random Fields
Conditional random fields (CRFs), which are popular supervised learning models for many natural language processing (NLP) tasks, typically require a large collection of labeled data for training. In practice, however, manual annotation of text documents is quite costly. Furthermore, even large labeled training sets can have arbitrarily limited performance peaks if they are not chosen with care. This paper considers the use of multi-criterion active learning for identification of a small but sufficient set of text samples for training CRFs. Our empirical results demonstrate that our method is capable of reducing the manual annotation costs, while also limiting the retraining costs that are often associated with active learning. In addition, we show that the generalization performance of CRFs can be enhanced through judicious selection of training examples
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