PolyIE: A Dataset of Information Extraction from Polymer Material
  Scientific Literature
Jerry Junyang Cheung Yuchen Zhuang Yinghao Li Pranav Shetty Wantian Zhao Sanjeev Grampurohit Rampi Ramprasad Chao Zhang
Scientific information extraction (SciIE), which aims to automatically
extract information from scientific literature, is becoming more important than
ever. However, there are no existing SciIE datasets for polymer materials,
which is an important class of materials used ubiquitously in our daily lives.
To bridge this gap, we introduce POLYIE, a new SciIE dataset for polymer
materials. POLYIE is curated from 146 full-length polymer scholarly articles,
which are annotated with different named entities (i.e., materials, properties,
values, conditions) as well as their N-ary relations by domain experts. POLYIE
presents several unique challenges due to diverse lexical formats of entities,
ambiguity between entities, and variable-length relations. We evaluate
state-of-the-art named entity extraction and relation extraction models on
POLYIE, analyze their strengths and weaknesses, and highlight some difficult
cases for these models. To the best of our knowledge, POLYIE is the first SciIE
benchmark for polymer materials, and we hope it will lead to more research
efforts from the community on this challenging task. Our code and data are
available on: https://github.com/jerry3027/PolyIE.