Cross-domain word representation aims to learn high-quality semantic representations in an under-resourced domain by leveraging information in a resourceful domain. However, most existing methods mainly transfer the semantics of common words across domains, ignoring the semantic relations among domain-specific words. In this paper, we propose a domain structure-based transfer learning method to learn cross-domain representations by leveraging the relations among domain-specific words. To accomplish this, we first construct a semantic graph to capture the latent domain structure using domain-specific co-occurrence information. Then, in the domain adaptation process, beyond domain alignment, we employ Laplacian Eigenmaps to ensure the domain structure is consistently distributed in the learned embedding space. As such, the learned cross-domain word representations not only capture shared semantics across domains, but also maintain the latent domain structure. We performed extensive experiments on two tasks, namely sentiment analysis and query expansion. The experiment results show the effectiveness of our method for tasks in under-resourced domains.

跨域词表示旨在通过利用资源丰富的域中的信息在资源不足的域中学习高质量的语义表示。然而，现有的大多数方法主要是跨领域转移常用词的语义，而忽略了特定领域词之间的语义关系。在本文中，我们提出了一种基于域结构的迁移学习方法，通过利用特定领域单词之间的关系来学习跨域表示。为了实现这一点，我们首先构建一个语义图来使用特定于域的共现信息来捕获潜在域结构。然后，在域适应过程中，除了域对齐之外，我们还使用拉普拉斯特征映射来确保域结构在学习的嵌入空间中一致分布。因此，学习到的跨域词表示不仅捕获跨域的共享语义，而且还维护潜​​在域结构。我们对两个任务进行了广泛的实验，即情感分析和查询扩展。实验结果表明了我们的方法在资源贫乏领域的任务的有效性。