情感分类是目前自然语言处理领域的一个热点研究问题。该文关注情感分类中的半监督学习方法(即基于少量标注样本和大量未标注样本进行学习的方式),提出了一种新的基于动态随机特征子空间的半监督学习方法。首先,动态生成多个随机特征子空间;然后,基于协同训练(Co-training)在每个特征子空间中挑选置信度高的未标注样本;最后使用这些挑选出的样本更新训练模型。实验结果表明我们的方法明显优于传统的静态产生方式及其他现有的半监督方法。此外该文还探索了特征子空间的划分数目问题。
Abstract
Recently, sentiment classification has become a hot research topic in Natural Language Processing. In this paper, we focus on semi-supervised learning paradigm for this task where only small amount of labeled data with many unlabeled samples are available for learning. Specifically, we propose a novel approach to semi-supervised learning for sentiment classification based on random subspace method. First, various random subspaces of the feature space are dynamically generated; Then, co-training algorithm is applied to choose high-confidential samples from the unlabeled data with the subspaces as the different views. Finally, the trained model is updated with the new obtained high-confidential samples. Experimental study across four product domains shows that our approach clearly outperforms the static way of the subspace generation and achieves much better performances than many other existing approaches for semi-supervised sentiment classification. In addition, this paper also explores the issues of different feature subspaces numbers.
Key wordssentiment classification; semi-supervised learning; feature subspace method
关键词
情感分类 /
半监督学习方法 /
特征子空间
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Key words
sentiment classification /
semi-supervised learning /
feature subspace method
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参考文献
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脚注
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基金
国家自然科学基金资助项目(61003155,60873150);模式识别国家重点实验室开发课题基金资助项目
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