Recognizing Citations in Public Comments
Bibliographic Data
| ID | 12972113 |
|---|---|
| Authors | Jaime Arguello (0000-0002-7645-0556, Carnegie Mellon University, corresponding author), Jamie Callan (Carnegie Mellon University), Stuart Shulman, Stuart W Shulman (University of Pittsburgh) |
| Year | 2008 |
| Volume | 5 |
| Issue | 1 |
| Pages | 49-71 |
| Publication date | 2008-07-14 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Information Technology & Politics (JOURNAL) |
| Journal identifiers | ISSN: 1933-169X • E-ISSN: 1933-1681 |
| Publisher | Routledge (PUBLISHER • GB) |
| DOI | 10.1080/19331680802153683 |
| OpenAlex | W2156190261 |
| Language | EN |
| Citations received | 1 |
| References cited | 10 |
Notice and comment rulemaking is central to how U.S. federal agencies craft new regulation. E-rulemaking, the process of soliciting and considering public comments that are submitted electronically, poses a challenge for agencies. The large volume of comments received makes it difficult to distill and address the most substantive concerns of the public. This work attempts to alleviate this burden by applying existing machine learning techniques to the problem of recognizing citation sentences. A citation in this context is defined as a statement in which the author of the public comment references an external source of factual information that is associated with a specific person or organization. The problem is formulated as a binary classification problem: Is a specific person or organization mentioned in a sentence being referenced as an external source of information? We show that our definition of a citation is reproducible by human judges and that citations can be detected using machine learning techniques with some success. Casting this as a machine learning problem requires selecting an appropriate representation of the sentence. Several feature sets are evaluated individually and in combination. Superior results are obtained by combining feature sets. Syntactic features, which characterize the structure of the sentence rather than its content, significantly improve accuracy when combined with other features, but not when used in isolation. Although prediction error rate is adequate, coverage could be improved. An error analysis enumerates short-term and long-term challenges that must be overcome to improve recall
Citation · Context (archaeology · Feature (linguistics · Information retrieval · Linguistics · Machine learning · Natural language processing · Notice · Paragraph · Political science · Process (computing · Representation (politics · Rulemaking · Sentence · Statement (logic · Term (time · World Wide Web · Computer Science · Law · Natural Language Processing Techniques · Software Engineering Research · Topic Modeling · Artificial Intelligence
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |