EBL can take a single training example and determine what are the relevant features in order to form a generalization.
EBL uses training examples to make searching for deductive consequences of a domain theory efficient in practice.
[8] The method has been successfully applied to several large-scale natural language parsing systems,[9] where the utility problem was solved by omitting the original grammar (domain theory) and using specialized LR-parsing techniques, resulting in huge speed-ups, at a cost in coverage, but with a gain in disambiguation.
[13] EBL can also be used to compile grammar-based language models for speech recognition, from general unification grammars.
Other people who worked on EBL for NLP include Guenther Neumann, Aravind Joshi, Srinivas Bangalore, and Khalil Sima'an.