Developing an Entity Extractor for the Scalable Constructing of Semantically Rich Socio-Technical Network Data

This project proposes to use supervised machine learning to build an entity extractor that is specifically designed for supporting the constructing of socio-technical network data. The resulting probabilistic prediction models and end-user technology are essential for being able to address substantive questions about real-world networks. The project team will make these outcomes publicly available to enable others to perform text coding projects, especially in the social sciences and humanities. We will also apply this extractor to multiple corpora for research projects.