Active learning for e-rulemaking: public comment categorization

We address the e-rulemaking problem of reducing the manual labor required to analyze public comment sets. In current and previous work, for example, text categorization techniques have been used to speed up the comment analysis phase of e-rulemaking -- by classifying sentences automatically, according to the rule-specific issues [2] or general topics that they address[7, 8]. Manually annotated data, however, is still required to train the supervised inductive learning algorithms that perform the categorization. This paper, therefore, investigates the application of active learning methods for public comment categorization: we develop two new, general-purpose, active learning techniques to selectively sample from the available training data for human labeling when building the sentence-level classifiers employed in public comment categorization. Using an e-rulemaking corpus developed for our purposes [2], we compare our methods to the well-known query by committee (QBC) active learning...

• Type: Conference
• Year: 2008
• Where: DGO
• Authors: Stephen Purpura, Claire Cardie, Jesse Simons
• Keywords: Active Learning, DGO 2008, Education, Public Comment, Public Comment Categorization