Elizabeth Rasch


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Development of Natural Language Processing Tools to Support Determination of Federal Disability Benefits in the U.S.
Bart Desmet | Julia Porcino | Ayah Zirikly | Denis Newman-Griffis | Guy Divita | Elizabeth Rasch
Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov)

The disability benefits programs administered by the US Social Security Administration (SSA) receive between 2 and 3 million new applications each year. Adjudicators manually review hundreds of evidence pages per case to determine eligibility based on financial, medical, and functional criteria. Natural Language Processing (NLP) technology is uniquely suited to support this adjudication work and is a critical component of an ongoing inter-agency collaboration between SSA and the National Institutes of Health. This NLP work provides resources and models for document ranking, named entity recognition, and terminology extraction in order to automatically identify documents and reports pertinent to a case, and to allow adjudicators to search for and locate desired information quickly. In this paper, we describe our vision for how NLP can impact SSA’s adjudication process, present the resources and models that have been developed, and discuss some of the benefits and challenges in working with large-scale government data, and its specific properties in the functional domain.