DMDD: A Large-Scale Dataset for Dataset Mentions Detection
Huitong Pan Qi Zhang Eduard Dragut Cornelia Caragea Longin Jan Latecki
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.