MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text
Tim O’Gorman, Zach Jensen, Sheshera Mysore, Kevin J. Huang, Rubayyat Mahbub, Elsa Olivetti, Andrew McCallum · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
Material science synthesis procedures are a promising domain for scientific NLP, as proper modeling of these recipes could provide insight into new ways of creating materials.However, a fundamental challenge in building information extraction models for material science synthesis procedures is getting accurate labels for the materials, operations, and other entities of those procedures.We present a new corpus of entity mention annotations over 595 Material Science synthesis procedural texts (157,488 tokens), which greatly expands the training data available for the Named Entity Recognition task.We outline a new label inventory designed to provide consistent annotations and a new annotation approach intended to maximize the consistency and annotation speed of domain experts.Inter-annotator agreement studies and baseline models trained upon the data suggest that the corpus provides high-quality annotations of these mention types.This corpus helps lay a foundation for future high-quality modeling of synthesis procedures.1337