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Active Learning for Cross-Domain Sentiment Classification

机译：主动学习的跨领域情感分类

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摘要

In the literature,various approaches have been proposed to address the domain adaptation problem in sentiment classification (also called cross-domain sentiment classification).However,the adaptation performance normally much suffers when the data distributions in the source and target domains differ significantly.In this paper,we suggest to perform active learning for cross-domain sentiment classification by actively selecting a small amount of labeled data in the target domain.Accordingly,we propose an novel active learning approach for cross-domain sentiment classification.First,we train two individual classifiers,i.e.,the source and target classifiers with the labeled data from the source and target respectively.Then,the two classifiers are employed to select informative samples with the selection strategy of Query By Committee (QBC).Third,the two classifier is combined to make the classification decision.Importantly,the two classifiers are trained by fully exploiting the unlabeled data in the target domain with the label propagation (LP) algorithm.Empirical studies demonstrate the effectiveness of our active learning approach for cross-domain sentiment classification over some strong baselines.
机译：在文献中，已提出了各种方法来解决情感分类中的域适应问题（也称为跨域情感分类）。但是，当源域和目标域中的数据分布明显不同时，适应性能通常会受到很大的影响。本文建议通过主动选择目标域中少量的标记数据来进行跨域情感分类的主动学习。因此，我们提出了一种新颖的跨域情感分类的主动学习方法。首先，我们训练两个单独的分类器，即分别带有来自源和目标的标记数据的源分类器和目标分类器。然后，使用这两个分类器通过“按委员会查询”（QBC）的选择策略来选择信息性样本。第三，这两个分类器是重要的是，通过充分利用未标记的标签来训练两个分类器。实证研究表明，我们的主动学习方法在一些强基准上对跨域情感分类的有效性。

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