The scale and scope of scholarly articles today are overwhelming human researchers who seek to timely digest and synthesize knowledge. In this paper, we seek to develop natural language processing (NLP) models to accelerate the speed of extraction of relationships from scholarly papers in social sciences, identify hypotheses from these papers, and extract the cause-and-effect entities. Specifically, we develop models to 1) classify sentences in scholarly documents in business and management as hypotheses (hypothesis classification), 2) classify these hypotheses as causal relationships or not (causality classification), and, if they are causal, 3) extract the cause and effect entities from these hypotheses (entity extraction). We have achieved high performance for all the three tasks using different modeling techniques. Our approach may be generalizable to scholarly documents in a wide range of social sciences, as well as other types of textual materials.

中文翻译：

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从社会科学学术论文中提取因果知识

当今学术文章的规模和范围压倒了寻求及时消化和综合知识的人类研究人员。在本文中，我们寻求开发自然语言处理 (NLP) 模型，以加快从社会科学学术论文中提取关系的速度，从这些论文中识别假设，并提取因果实体。具体来说，我们开发模型以 1) 将商业和管理学术文件中的句子分类为假设（假设分类），2）将这些假设分类为因果关系与否（因果关系分类），并且，如果它们是因果关系，3）提取来自这些假设的因果实体（实体提取）。我们使用不同的建模技术在所有三个任务中都实现了高性能。