论文标题
找到资金：实体与不完整的资金知识库联系
Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases
论文作者
论文摘要
从学术文章中自动提取资金信息为行业和研究社区增添了重要价值，例如基于收到的资金和支持开放访问政策来追踪资助组织，资助组织和大学的研究成果。识别和链接资金实体的两个主要挑战是：（i）知识库（KB）的稀疏图结构，这使得通常使用的基于图的实体链接方法的依次用于资助域，（ii）KB中的缺失实体（与最近的零击方法）（不同的零击方法）需要标记不带KB条目的零件。我们提出了一个可以执行零预测并克服数据稀缺问题的实体链接模型。我们的模型建立在基于变压器的提及检测和双重编码模型上，以执行实体链接。我们表明，我们的模型表现优于现有的基线。
Automatic extraction of funding information from academic articles adds significant value to industry and research communities, such as tracking research outcomes by funding organizations, profiling researchers and universities based on the received funding, and supporting open access policies. Two major challenges of identifying and linking funding entities are: (i) sparse graph structure of the Knowledge Base (KB), which makes the commonly used graph-based entity linking approaches suboptimal for the funding domain, (ii) missing entities in KB, which (unlike recent zero-shot approaches) requires marking entity mentions without KB entries as NIL. We propose an entity linking model that can perform NIL prediction and overcome data scarcity issues in a time and data-efficient manner. Our model builds on a transformer-based mention detection and bi-encoder model to perform entity linking. We show that our model outperforms strong existing baselines.
