论文标题
基于相关研究部分的术语功能的新引用推荐策略
A New Citation Recommendation Strategy Based on Term Functions in Related Studies Section
论文作者
论文摘要
目的：研究人员在撰写科学文章时会经常遇到以下问题：（1）选择适当的引用来支持研究思想是具有挑战性的。 （2）文献综述并未进行广泛的进行，这导致解决了其他人已经解决的研究问题。这项研究通过应用引文背景的术语函数来侧重于引文建议，从而有可能提高撰写文献综述的效率。设计/方法论/方法：我们提供了九个术语功能，并从现有文献中确定了三个新创建和六个函数。使用这些术语函数作为标签，我们在三个主题中注释了531个研究论文，以评估我们提出的建议策略。使用VSM的BM25和Word2Vec作为建议的基线模型实现。然后应用术语函数信息以增强性能。研究结果：实验表明，基于术语的方法的表现优于召回，精度和F1得分测量的基线方法，表明术语函数可用于识别有价值的引用。研究局限性：由于相关研究部分中段落的注释引文功能的复杂性，数据集不足。应进行最新的深度学习模型，以验证所提出的方法。实际含义：引文推荐策略可能有助于有价值的引文发现，语义科学检索和自动文献审查生成。原创性/价值：提出的基于引用功能的引文建议可以为用户对结果产生直观的解释，从而提高推荐系统的透明度，说服力和有效性。
Purpose: Researchers frequently encounter the following problems when writing scientific articles: (1) Selecting appropriate citations to support the research idea is challenging. (2) The literature review is not conducted extensively, which leads to working on a research problem that others have well addressed. This study focuses on citation recommendation in the related studies section by applying the term function of a citation context, potentially improving the efficiency of writing a literature review. Design/methodology/approach: We present nine term functions with three newly created and six identified from existing literature. Using these term functions as labels, we annotate 531 research papers in three topics to evaluate our proposed recommendation strategy. BM25 and Word2vec with VSM are implemented as the baseline models for the recommendation. Then the term function information is applied to enhance the performance. Findings: The experiments show that the term function-based methods outperform the baseline methods regarding the recall, precision, and F1-score measurement, demonstrating that term functions are useful in identifying valuable citations. Research limitations: The dataset is insufficient due to the complexity of annotating citation functions for paragraphs in the related studies section. More recent deep learning models should be performed to future validate the proposed approach. Practical implications: The citation recommendation strategy can be helpful for valuable citation discovery, semantic scientific retrieval, and automatic literature review generation. Originality/value: The proposed citation function-based citation recommendation can generate intuitive explanations of the results for users, improving the transparency, persuasiveness, and effectiveness of recommender systems.
