文章基本信息

标题：TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics
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作者：Yufang Hou ; Charles Jochim ; Martin Gleize 等
期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2021
卷号：2021
页码：707-714
DOI：10.18653/v1/2021.eacl-main.59
语种：English
出版社：ACL Anthology
摘要：Tasks, Datasets and Evaluation Metrics are important concepts for understanding experimental scientific papers. However, 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 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.