PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature
This provides a new benchmark for researchers in materials science and NLP to advance information extraction from polymer literature, though it is incremental as it extends existing SciIE methods to a new domain.
The authors tackled the lack of scientific information extraction datasets for polymer materials by introducing POLYIE, a dataset curated from 146 full-length articles with expert annotations for named entities and N-ary relations, and they evaluated state-of-the-art models on it, highlighting challenges and difficult cases.
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.