  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.
