Valet: Rule-Based Information Extraction for Rapid Deployment

Dayne Freitag, John Cadigan, Robert Sasseen, Paul Kalmar


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
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
524–533
Language:
URL:
https://aclanthology.org/2022.lrec-1.55
DOI:
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.lrec-1.55.pdf