Relationship Extraction: Automatic Information Extraction and Organization for Supporting Analysts in Threat Assessment
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
In order for analysts to be able to do their work, they sift through hundreds, thousands, or even millions of documents to make connections between entities of interest. This process is time consuming, tedious, and prone to potential error from missed connections or connections made that should not have been. There exist many tools in natural language processing, or NLP, to extract information from documents. However, when it comes to relationship extraction, there has been varied success. This project began with a goal to solve the relationship extraction problem which developed into a deeper understanding of the problem and the associated challenges for solving this problem on a general scale. In this report, we explain our research and approach to relationship extraction, identify other auxiliary problems in NLP that provide additional challenges to solving relationship extraction generally, explain our analysis of the current state of relationship extraction, and postulate future work to address these problems.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- NA0003525
- OSTI ID:
- 1832526
- Report Number(s):
- SAND2021-13329; 701974
- Country of Publication:
- United States
- Language:
- English
Similar Records
Identification of threats using linguistics-based knowledge extraction.
Learning from Correlated Events for Equipment Relation Inference in Buildings