  Tasks, Datasets and Evaluation Metrics are important concepts for
understanding experimental scientific papers. However, most previous work on
information extraction for scientific literature mainly focuses on the
abstracts only, and does not treat datasets as a separate type of entity (Zadeh
and Schumann, 2016; Luan et al., 2018). In this paper, we present a new corpus
that contains domain expert annotations for Task (T), Dataset (D), Metric (M)
entities on 2,000 sentences extracted from NLP papers. We report experiment
results on TDM extraction using a simple data augmentation strategy and apply
our tagger to around 30,000 NLP papers from the ACL Anthology. The corpus is
made publicly available to the community for fostering research on scientific
publication summarization (Erera et al., 2019) and knowledge discovery.
