Using Machine Learning to Improve Public Reporting on U.S. Government Contracts

The U.S. government procures more than $500 billion annually in goods and services on public contracts, which it classifies using a hierarchical product and service taxonomy. Classification serves several purposes, including transparency in the use of taxpayer funding; reporting, tracing, and segmenting government expenditures; budgeting; and forecasting. Government acquisition personnel have historically performed these classifications manually, resulting in a process that is time-consuming and error-prone and offers limited visibility into government purchases. The problem faced is not unique to the public sector and is common across retail, manufacturing, and healthcare, among other settings. Using almost 4 million historical data records on governmental purchases, we fit a series of classifiers and demonstrate (a) superior performance when explicitly modeling the hierarchical structure of information domains through the use of top-down strategies and (b) the effectiveness of character-level convolutional neural networks when textual inputs are terse and contain irregularities such as abnormal character combinations and misspellings, which are common in government contracts. Our machine learning models are embedded in multiple software applications, including a web application that we developed, used by federal government personnel and other contracting professionals.