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Scientific action graphs extraction from materials synthesis procedures isimportant for reproducible research, machine automation, and materialprediction. But the lack of annotated data has hindered progress in this field.We demonstrate an effort to annotate Polycrystalline Materials SynthesisProcedures (PcMSP) from 305 open access scientific articles for theconstruction of synthesis action graphs. This is a new dataset for materialscience information extraction that simultaneously contains the synthesissentences extracted from the experimental paragraphs, as well as the entitymentions and intra-sentence relations. A two-step human annotation andinter-annotator agreement study guarantee the high quality of the PcMSP corpus.We introduce four natural language processing tasks: sentence classification,named entity recognition, relation classification, and joint extraction ofentities and relations. Comprehensive experiments validate the effectiveness ofseveral state-of-the-art models for these challenges while leaving large spacefor improvement. We also perform the error analysis and point out some uniquechallenges that require further investigation. We will release our annotationscheme, the corpus, and codes to the research community to alleviate thescarcity of labeled data in this domain.

PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text