Near-duplicate detection for eRulemaking
Year: 2005
Type: conference-paper
Abstract: U.S. regulatory agencies are required to solicit, consider, and respond to public comments before issuing regulations. In recent years, agencies have begun to accept comments via both email and Web forms. The transition from paper to electronic comments makes it much easier for individuals to customize form letters, which they do, creating comments that express the same viewpoint in slightly different languages. This paper explores the use of simple text clustering and retrieval algorithms for identifying near-duplicate public comments. Experiments with public comments about a recent regulation proposed by the Environmental Protection Agency (EPA) demonstrate the effectiveness of the algorithms.
Authors: Hui Yang, Jamie Callan
Institution: Carnegie Mellon University
Language: English
Topic: Data Quality and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W1575770195