PcMSP：从多晶材料合成程序文本中提取科学行为图的数据集
PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text
摘要：
从材料合成过程中提取科学行为图对于可重复性研究、机器自动化和材料预测非常重要。但由于缺乏注释数据，阻碍了这一领域的进展。我们证明了一个努力，注释多晶材料合成程序（PcMSP）从305开放获取的科学文章的合成动作图的建设。这是一个新的材料科学信息抽取数据集，同时包含从实验段落中提取的合成句子，以及实体提及和句内关系。两步人工标注和标注者间一致性研究保证了PcMSP语料库的高质量。我们介绍了四个自然语言处理任务：句子分类，命名实体识别，关系分类，实体和关系的联合提取。综合实验验证了几种最先进的模型对这些挑战的有效性，同时留下了很大的改进空间。我们还进行了错误分析，并指出了一些独特的挑战，需要进一步调查。我们将向研究社区发布我们的注释方案，语料库和代码，以缓解该领域标记数据的稀缺性。
英文摘要：
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.
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