The recognition of dataset names is a critical task for automatic information
extraction in scientific literature, enabling researchers to understand and
identify research opportunities. However, existing corpora for dataset mention
detection are limited in size and naming diversity. In this paper, we introduce
the Dataset Mentions Detection Dataset (DMDD), the largest publicly available
corpus for this task. DMDD consists of the DMDD main corpus, comprising 31,219
scientific articles with over 449,000 dataset mentions weakly annotated in the
format of in-text spans, and an evaluation set, which comprises of 450
scientific articles manually annotated for evaluation purposes. We use DMDD to
establish baseline performance for dataset mention detection and linking. By
analyzing the performance of various models on DMDD, we are able to identify
open problems in dataset mention detection. We invite the community to use our
dataset as a challenge to develop novel dataset mention detection models.

该研究介绍了 DMDD 数据集，它是目前用于科学文献数据集命名检测的最大公开语料库。通过对不同检测模型在 DMDD 上的表现进行分析，研究发现了数据集命名检测方面的一些问题，邀请社区使用该数据集开发新的检测模型。