This paper studies the end-to-end construction of an NLP Knowledge Graph (KG)
from scientific papers. We focus on extracting four types of relations:
evaluatedOn between tasks and datasets, evaluatedBy between tasks and
evaluation metrics, as well as coreferent and related relations between the
same type of entities. For instance, F1-score is coreferent with F-measure. We
introduce novel methods for each of these relation types and apply our final
framework (SciNLP-KG) to 30,000 NLP papers from ACL Anthology to build a
large-scale KG, which can facilitate automatically constructing scientific
leaderboards for the NLP community. The results of our experiments indicate
that the resulting KG contains high-quality information.

这篇论文研究了从科学论文中构建 NLP 知识图谱的端到端构建方法，重点在提取四种类型的关系：任务和数据集之间的关系，任务和评估指标之间的关系，以及相同类型实体之间的关联关系。通过将 SciNLP-KG 框架应用于 ACL 文献库中的 30,000 篇 NLP 论文构建了一个大规模的知识图谱，可支持自动构建 NLP 领域的科学排行榜，并且实验结果表明：所得知识图谱包含高品质信息。