  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.
