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首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >Transfer Learning by Linking Similar Feature Clusters for Sentiment Classification
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Transfer Learning by Linking Similar Feature Clusters for Sentiment Classification

机译：通过链接相似特征类进行情感分类的转移学习

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Transfer learning aims to extract the knowledge from a label-rich source domain to enhance the predictive model of a target domain. Previous methods achieve knowledge transfer by detecting a shared low-dimensional feature representation from source domain to target domain. Along this line, many algorithms, e.g., dual transfer learning (DTL), triplex transfer learning (TRi-TL) etc., have been proposed and widely used for text classification. However, we argue that it is difficult for models to distinguish exactly the common concepts or identical concepts across different domains through the existing algorithms, even though source and target domains are related but different. So we propose to use the similar feature clusters as knowledge transfer, that is, we only guarantee the approximate similarity of common word clusters across different domains, rather than the exactly same. Based on the above assumption, the derived association matrices between word clusters and document classes should be slightly different to account for the word clusters variations. To take the above assumptions into account, we propose a novel Nonnegative Matrix Tri-Factorization based transfer learning by linking similar feature clusters (LSF-TL) for sentiment classification, in which an approximate constraint between similar word clusters matrices is added to allow differences while keeping the knowledge transferring function. Besides, LSF-TL also provides the same approximate constraint for the derived clusters association matrices. Then we employ an iterative updating algorithm with sound theoretical proof to find the local optimal solution. Last, we evaluate our method by conducting extensive experiments on Amazon product reviews. The results show that our approach achieves better classification accuracy than the state-of-the-art methods for both Cross-lingual sentiment classification(CLSC) and Cross-lingual cross-domain sentiment classification(CLCDSC) tasks.
机译：转移学习旨在从标签丰富的源域中提取知识，以增强目标域的预测模型。先前的方法通过检测从源域到目标域的共享低维特征表示来实现知识转移。沿着这条思路，已经提出了许多算法，例如，双转移学习（DTL），三重转移学习（TRi-TL）等，并且广泛用于文本分类。但是，我们认为，即使源域和目标域是相关的但又不同，模型也很难通过现有算法在不同域之间准确地区分相同的概念或相同的概念。因此，我们建议使用相似的特征簇作为知识转移，也就是说，我们仅保证跨不同领域的通用词簇的近似相似性，而不是完全相同。基于以上假设，得出的词簇与文档类别之间的关联矩阵应略有不同，以说明词簇的变化。考虑到上述假设，我们通过链接相似特征簇（LSF-TL）进行情感分类，提出了一种新颖的基于非负矩阵三因子化的迁移学习方法，其中在相似词簇矩阵之间添加了近似约束以允许差异，同时保持知识传递功能。此外，LSF-TL还为导出的聚类关联矩阵提供了相同的近似约束。然后我们采用具有良好理论证明的迭代更新算法来找到局部最优解。最后，我们通过对亚马逊产品评论进行广泛的实验来评估我们的方法。结果表明，与跨语言情感分类（CLSC）和跨语言跨域情感分类（CLCDSC）任务的最新方法相比，我们的方法具有更好的分类准确性。

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