通过主动学习改进文本分类中的概率模型
Improving Probabilistic Models in Text Classification via Active Learning
AI总结：
本文提出结合概率模型与主动学习的文本分类算法，利用有标签和无标签数据并优先标注难例，以少量标注与计算成本达到先进性能，并提供开源 activeText。
摘要：
社会科学家经常对文本文档进行分类，以便将所得标签用作实证研究中的结果或预测变量。自动化文本分类由于所需人工编码较少，已成为一种标准工具。然而，学者们仍需要许多人工标注文档来训练自动化分类器。为降低标注成本，我们提出了一种新的文本分类算法，将概率模型与主动学习相结合。该概率模型同时使用有标签和无标签数据，而主动学习则将标注工作集中于难以分类的文档。我们的验证研究表明，本算法的分类性能可与最先进方法相媲美，而计算成本仅为其一小部分。此外，我们复现了两篇近期发表的论文，仅使用这些研究原始标注数据中的很小一部分，便得出了相同的实质性结论。我们提供了 activeText，这是一款用于实现本文方法的开源软件。
英文摘要：
Social scientists often classify text documents to use the resulting labels as an outcome or a predictor in empirical research. Automated text classification has become a standard tool, since it requires less human coding. However, scholars still need many human-labeled documents to train automated classifiers. To reduce labeling costs, we propose a new algorithm for text classification that combines a probabilistic model with active learning. The probabilistic model uses both labeled and unlabeled data, and active learning concentrates labeling efforts on difficult documents to classify. Our validation study shows that the classification performance of our algorithm is comparable to state-of-the-art methods at a fraction of the computational cost. Moreover, we replicate two recently published articles and reach the same substantive conclusions with only a small proportion of the original labeled data used in those studies. We provide activeText, an open-source software to implement our method.
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。
