基于多元损失函数的文本量词优化
Optimizing Text Quantifiers for Multivariate Loss Functions
摘要：
我们解决问题的\emph{quantification}，监督学习任务，其目标是，给定一个类，估计的相对频率（或\emph{prevalence}）的类在数据集中的未标记的项目。量化在数据和文本挖掘中有几个应用，例如在给定产品的一组评论中估计正面评论的普遍性，或者在技术支持电话记录的数据集中估计给定支持问题的普遍性。到目前为止，量化已经通过学习通用分类器，计算已分配类别的未标记项，并根据一些算法调整获得的计数来解决。在本文中，我们离开传统的使用通用分类器，而不是使用监督学习模型\emph{structured prediction}，能够生成分类器直接优化的（多变量和非线性）功能用于评估量化精度。我们在5500个二进制高维数据集（平均每个数据集超过14，000个文档）上运行的实验表明，该方法比现有的最先进的量化方法更准确，更稳定，更有效。
英文摘要：
We address the problem of \emph{quantification}, a supervised learning task whose goal is, given a class, to estimate the relative frequency (or \emph{prevalence}) of the class in a dataset of unlabelled items. Quantification has several applications in data and text mining, such as estimating the prevalence of positive reviews in a set of reviews of a given product, or estimating the prevalence of a given support issue in a dataset of transcripts of phone calls to tech support. So far, quantification has been addressed by learning a general-purpose classifier, counting the unlabelled items which have been assigned the class, and tuning the obtained counts according to some heuristics. In this paper we depart from the tradition of using general-purpose classifiers, and use instead a supervised learning model for \emph{structured prediction}, capable of generating classifiers directly optimized for the (multivariate and non-linear) function used for evaluating quantification accuracy. The experiments that we have run on 5500 binary high-dimensional datasets (averaging more than 14,000 documents each) show that this method is more accurate, more stable, and more efficient than existing, state-of-the-art quantification methods.
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。
