In the era of big scholarly data, researchers frequently encounter the following problems when writing scientific articles: 1) it's challenging to select appropriate references to support the research idea, and 2) literature review is not conducted extensively, which leads to working on a research problem that has been well addressed by others. Citation recommendation assists researchers to decide which article should be cited in a timely manner, as well as perform comprehensive and high-quality review of scientific literature. Some work has been done on this valuable and challenging task, but few of them focused on applying the semantic information of the citation context. This paper proposes a new citation recommendation strategy based on term function - the functions or roles of citation context in related studies section. We present 9 term functions as identified from the literature and annotated 531 research papers in 3 areas to evaluate our approach. The experiment results demonstrate that term functions are effective to identifying valuable references. The proposed method recommends more accurate citations for a given topic when compared to several baseline methods. The citation recommendation strategy can be helpful to generate automatic summaries and literature reviews.

中文翻译：

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一种新的基于相关研究部分术语函数的引文推荐策略

在学术大数据时代，研究人员在撰写科学文章时经常遇到以下问题：1）选择合适的参考文献来支持研究思路具有挑战性；2）文献综述不广泛，导致研究工作别人已经很好解决的问题。引文推荐帮助研究人员及时决定应该引用哪篇文章，以及对科学文献进行全面、高质量的审查。在这项有价值且具有挑战性的任务上已经做了一些工作，但很少有人专注于应用引文上下文的语义信息。本文提出了一种新的基于术语功能的引文推荐策略——引文上下文在相关研究部分的功能或作用。我们提供了从文献中确定的 9 个术语函数，并注释了 3 个领域的 531 篇研究论文来评估我们的方法。实验结果表明，术语函数对于识别有价值的参考是有效的。与几种基线方法相比，所提出的方法为给定主题推荐更准确的引文。引文推荐策略有助于生成自动摘要和文献综述。