Language processing technologies for electronic rulemaking: a project highlight

ABSTRACT

In this project, we are developing new text processing tools that help people perform advanced analysis of large collections of text commentary. This problem is increasingly faced by the United States federal government's regulation writers who formulate the rules and regulations that define the details of laws enacted by Congress. Our research focuses on text clustering, text searching using information retrieval, near-duplicate detection, opinion identification, stakeholder characterization, and extractive summarization, as well as the impact of such tools on the process of rulemaking itself. Versions of a Rule-Writer's Workbench will be built by Computer Science researchers at ISI and CMU, deployed annually for experimental use by our government partners, and evaluated by social science researchers from the Library and Information Science and Sociology departments at the Universities of Pittsburgh and San Francisco respectively. This three-year project started in October 2004 and is funded under the National Science Foundation's Digital Government program.

REFERENCES

• 1: Hui Yang , Jamie Callan, Near-duplicate detection for eRulemaking, Proceedings of the 2005 national conference on Digital government research, May 15-18, 2005, Atlanta, Georgia
• 2: The eRulemaking Research Group project home page is at: http://erulemaking.ucsur.pitt.edu
• 3: The eRulemaking Testbed is at: http://hartford.lti.cs.cmu.edu/eRulemaking/Data.html

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms, Human Factors

Keywords:
federal government, information retrieval, natural language processing, near-duplicate detection, opinion recognition, regulations, rulemaking, text annotation