# Argument Identification in Public Comments from eRulemaking

## TL;DR

This paper develops a taxonomy and machine learning system to automatically identify, classify, and determine the stance of argumentative text in public comments during the eRulemaking process, aiding agencies in managing large volumes of feedback.

## Contribution

It introduces a new taxonomy of argument claims, creates a large annotated dataset, and builds a hierarchical classification model for argument identification and stance detection.

## Key findings

- Successful automatic identification of argumentative spans
- Effective classification of argument claim types
- Accurate stance determination in comments

## Abstract

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.