A study in rule-specific issue categorization for e-rulemaking

We address the e-rulemaking problem of categorizing public comments according to the issues that they address. In contrast to previous text categorization research in e-rulemaking [5, 6], and in an attempt to more closely duplicate the comment analysis process in federal agencies, we employ a set of rule-specific categories, each of which corresponds to a significant issue raised in the comments. We describe the creation of a corpus to support this text categorization task and report interannotator agreement results for a group of six annotators. We outline those features of the task and of the e-rulemaking context that engender both a non-traditional text categorization corpus and a correspondingly difficult machine learning problem. Finally, we investigate the application of standard and hierarchical text categorization techniques to the e-rulemaking data sets and find that automatic categorization methods show promise as a means of reducing the manual labor required to analyze larg...

• Type: Conference
• Year: 2008
• Where: DGO
• Authors: Claire Cardie, Cynthia Farina, Adil Aijaz, Matt Rawding, Stephen Purpura
• Keywords: DGO 2008, Education, Text Categorization, Text Categorization Research, Text Categorization Task