基于外围上下文和时间演化增强的内联引文分类
Inline Citation Classification using Peripheral Context and Time-evolving Augmentation
摘要：
引文在确定研究文章之间的关联方面起着关键作用。它描绘了指示性，支持性或对比研究的基本信息。内联引文分类的任务有助于推断这些关系;然而，现有的研究仍然不成熟，需要进一步审查。当前用于内联引文分类的数据集和方法仅使用引文标记的句子，限制了模型对领域知识和相邻上下文句子的视而不见。在本文中，我们提出了一个新的数据集，命名为3Cext，它与被引用的句子，提供话语信息使用的邻近句子分析对比和蕴涵关系以及域信息。我们提出了Pericite，一个基于transformer的深度神经网络，融合了外围句子和领域知识。我们的模型在3Cext数据集上达到了最先进的水平，与最佳基线相比达到了+0.09 F1。我们进行了广泛的消融，以分析所提出的数据集和模型融合方法的有效性。
英文摘要：
Citation plays a pivotal role in determining the associations among research articles. It portrays essential information in indicative, supportive, or contrastive studies. The task of inline citation classification aids in extrapolating these relationships; However, existing studies are still immature and demand further scrutiny. Current datasets and methods used for inline citation classification only use citation-marked sentences constraining the model to turn a blind eye to domain knowledge and neighboring contextual sentences. In this paper, we propose a new dataset, named 3Cext, which along with the cited sentences, provides discourse information using the vicinal sentences to analyze the contrasting and entailing relationships as well as domain information. We propose PeriCite, a Transformer-based deep neural network that fuses peripheral sentences and domain knowledge. Our model achieves the state-of-the-art on the 3Cext dataset by +0.09 F1 against the best baseline. We conduct extensive ablations to analyze the efficacy of the proposed dataset and model fusion methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
