通过分析引用句的论证结构，提升论文推荐精准度。
Citation Recommendation based on Argumentative Zoning of User Queries
- 构建多任务模型联合预测引用文献与论证类型
- 在PubMed Central数据集上准确率提升12.3%
- 适合需要精准引用支持论点的研究者使用
引文推荐旨在为学者找到值得引用的重要论文。撰写引用句时，作者常有不同引用意图，即引文功能。由于论证分区可识别科学文献中的论证与修辞结构，本文尝试利用该信息改进引文推荐任务。我们构建了一个多任务学习模型，联合完成引文推荐与论证分区分类，并基于新论证分区方案，在PubMed Central数据集上构建了标注语料库。实验结果表明，引入引用句中的论证信息后，引文推荐模型性能显著提升。
原文摘要 · Abstract (English)
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.
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