Argument Identification in Public Comments from eRulemaking
This addresses the challenge for administrative agencies in efficiently processing millions of public comments, though it is incremental as it builds on existing text classification methods.
The paper tackles the problem of identifying and classifying arguments in public comments from eRulemaking, proposing a taxonomy, creating a dataset of millions of annotated sentences, and building a hierarchical classification model for automatic analysis.
Administrative agencies in the United States receive millions of comments each year concerning proposed agency actions during the eRulemaking process. These comments represent a diversity of arguments in support and opposition of the proposals. While agencies are required to identify and respond to substantive comments, they have struggled to keep pace with the volume of information. In this work we address the tasks of identifying argumentative text, classifying the type of argument claims employed, and determining the stance of the comment. First, we propose a taxonomy of argument claims based on an analysis of thousands of rules and millions of comments. Second, we collect and semi-automatically bootstrap annotations to create a dataset of millions of sentences with argument claim type annotation at the sentence level. Third, we build a system for automatically determining argumentative spans and claim type using our proposed taxonomy in a hierarchical classification model.