Citation recommendation aims to locate the important papers for scholars to cite. When writing the citing sentences, the authors usually hold different citing intents, which are referred to citation function in citation analysis. Since argumentative zoning is to identify the argumentative and rhetorical structure in scientific literature, we want to use this information to improve the citation recommendation task. In this paper, a multi-task learning model is built for citation recommendation and argumentative zoning classification. We also generated an annotated corpus of the data from PubMed Central based on a new argumentative zoning schema. The experimental results show that, by considering the argumentative information in the citing sentence, citation recommendation model will get better performance.

翻译：引文推荐旨在为学者定位需要引用的重要文献。在撰写引用语句时，作者通常持有不同的引用意图，这在引文分析中被称为引用功能。由于论证分区旨在识别科学文献中的论证与修辞结构，我们希望利用这一信息来改进引文推荐任务。本文构建了一个多任务学习模型，用于引文推荐与论证分区分类。同时，基于新的论证分区框架，我们构建了一个来自PubMed Central的标注语料库。实验结果表明，通过考虑引用语句中的论证信息，引文推荐模型能获得更优的性能。