Dayne Freitag, John Cadigan, Robert Sasseen, Paul Kalmar; Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), Marseille, France
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.
Keywords: information extraction, rule-based methods