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

ABSTRACT: Scientific action graphs extraction from materials synthesis procedures is
important for reproducible research, machine automation, and material
prediction. But the lack of annotated data has hindered progress in this field.
We demonstrate an effort to annotate Polycrystalline Materials Synthesis
Procedures (PcMSP) from 305 open access scientific articles for the
construction of synthesis action graphs. This is a new dataset for material
science information extraction that simultaneously contains the synthesis
sentences extracted from the experimental paragraphs, as well as the entity
mentions and intra-sentence relations. A two-step human annotation and
inter-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 of
entities and relations. Comprehensive experiments validate the effectiveness of
several state-of-the-art models for these challenges while leaving large space
for improvement. We also perform the error analysis and point out some unique
challenges that require further investigation. We will release our annotation
scheme, the corpus, and codes to the research community to alleviate the
scarcity of labeled data in this domain.
Scientific action graphs extraction from materials synthesis procedures is
important for reproducible research, machine automation, and material
prediction. But the lack of annotated data has hindered progress in this field.
We demonstrate an effort to annotate Polycrystalline Materials Synthesis
Procedures (PcMSP) from 305 open access scientific articles for the
construction of synthesis action graphs. This is a new dataset for material
science information extraction that simultaneously contains the synthesis
sentences extracted from the experimental paragraphs, as well as the entity
mentions and intra-sentence relations. A two-step human annotation and
inter-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 of
entities and relations. Comprehensive experiments validate the effectiveness of
several state-of-the-art models for these challenges while leaving large space
for improvement. We also perform the error analysis and point out some unique
challenges that require further investigation. We will release our annotation
scheme, the corpus, and codes to the research community to alleviate the
scarcity of labeled data in this domain.
PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text
http://arxiv.org/abs/2210.12401v1