Abstract
We consider whether machine models can facilitate the human development of rule sets for information extraction. Arguing that rule-based methods possess a speed advantage in the early development of new extraction capabilities, we ask whether this advantage can be increased further through the machine facilitation of common recurring manual operations in the creation of an extraction rule set from scratch. Using a historical rule set, we reconstruct and describe the putative manual operations required to create it. In experiments targeting one key operation—the enumeration of words occurring in particular contexts—we simulate the process or corpus review and word list creation, showing that several simple interventions greatly improve recall as a function of simulated labor.- Anthology ID:
- 2022.pandl-1.6
- Volume:
- Proceedings of the First Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Laura Chiticariu, Yoav Goldberg, Gus Hahn-Powell, Clayton T. Morrison, Aakanksha Naik, Rebecca Sharp, Mihai Surdeanu, Marco Valenzuela-Escárcega, Enrique Noriega-Atala
- Venue:
- PANDL
- SIG:
- Publisher:
- International Conference on Computational Linguistics
- Note:
- Pages:
- 45–55
- Language:
- URL:
- https://aclanthology.org/2022.pandl-1.6
- DOI:
- Cite (ACL):
- Dayne Freitag, John Cadigan, John Niekrasz, and Robert Sasseen. 2022. Accelerating Human Authorship of Information Extraction Rules. In Proceedings of the First Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning, pages 45–55, Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
- Cite (Informal):
- Accelerating Human Authorship of Information Extraction Rules (Freitag et al., PANDL 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.pandl-1.6.pdf