Lower-dimensional representation of scientific text has attracted much attention among researchers due to its impact on many data mining and recommendation tasks. This paper studies two main research streams in scientific literature representation. First, both local and distributed representation viewpoints are reviewed and their advantages and disadvantages in lower dimensional representation are discussed. The paper then proposes a novel hybrid distributed technique for text representation. Using scientific articles as the major source of textual information, both the article’s content and citation network are used to build a distributed and universal lower dimensional representation. The superiority of the new technique to the traditional methods is then justified in predicting the existence of links in large citation graphs.

科学文本的低维表示形式由于对许多数据挖掘和推荐任务的影响而引起了研究人员的广泛关注。本文研究了科学文献表征中的两个主要研究流。首先，回顾了局部和分布式表示观点，并讨论了它们在低维表示中的优缺点。然后，本文提出了一种新颖的混合分布式技术，用于文本表示。利用科学文章作为文本信息的主要来源，文章的内容和引文网络都可用于构建分布式的通用低维表示形式。然后，在预测大型引用图中链接的存在时，就可以证明新技术相对于传统方法的优越性。