Abstract
We present VALET, a framework for rule-based information extraction written in Python. VALET departs from legacy approaches predicated on cascading finite-state transducers, instead offering direct support for mixing heterogeneous information–lexical, orthographic, syntactic, corpus-analytic–in a succinct syntax that supports context-free idioms. We show how a handful of rules suffices to implement sophisticated matching, and describe a user interface that facilitates exploration for development and maintenance of rule sets. Arguing that rule-based information extraction is an important methodology early in the development cycle, we describe an experiment in which a VALET model is used to annotate examples for a machine learning extraction model. While learning to emulate the extraction rules, the resulting model generalizes them, recognizing valid extraction targets the rules failed to detect.- Anthology ID:
- 2022.lrec-1.55
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 524–533
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.55
- DOI:
- Cite (ACL):
- Dayne Freitag, John Cadigan, Robert Sasseen, and Paul Kalmar. 2022. Valet: Rule-Based Information Extraction for Rapid Deployment. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 524–533, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Valet: Rule-Based Information Extraction for Rapid Deployment (Freitag et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.lrec-1.55.pdf