We describe the creation of a corpus that supports a real-world hierarchical text categorization task in the domain of electronic rulemaking (eRulemaking). Features of the task and of the eRulemaking domain engender both a non-traditional text categorization corpus and a correspondingly difficult machine learning task. Interannotator agreement results are presented for a group of six annotators. We also briefly describe the results of experiments that apply standard and hierarchical text categorization techniques to the eRulemaking data sets. The corpus is the first in a series of related sentence-level text categorization corpora to be developed in the eRulemaking domain.
Cardie, Claire; Farina, Cynthia R.; Rawding, Matt; and Aijaz, Adil, "An eRulemaking Corpus: Identifying Substantive Issues in Public Comments" (2008). Cornell e-Rulemaking Initiative Publications. Paper 6.