AAAI Fall Symposium 2017

Cognitive Assistance for Automating the Analysis of the Federal Acquisition Regulations System

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Government regulations are critical to understanding how to do business with a government entity and receive other benefits. However, government regulations are also notoriously long and organized in ways that can be confusing for novice users. Developing cognitive assistance tools that remove some of the burden from human users is of potential benefit to a variety of users. The volume of data found in United States federal government regulation suggests a multiple-step approach to process the data into machinereadable text, create an automated legal knowledge base capturing various facts and rules, and eventually building a legal question and answer system to acquire understanding from various regulations and provisions. Our work discussed in this paper represents our initial efforts to build a framework for Federal Acquisition Regulations System (Title 48, Code of Federal Regulations) in order to create an efficient legal knowledge base representing relationships between various legal elements, semantically similar terminologies, deontic expressions and cross-referenced legal facts and rules.


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deep learning, natural language processing

InProceedings

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