Learning Parse and Translation Decisions From Examples With Rich Context (1997)
This paper presents a knowledge and context-based system for parsing and translating natural language and evaluates it on sentences from the Wall Street Journal. Applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context, as encoded in features which describe the morpholgical, syntactical, semantical and other aspects of a given parse state.
In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL'97/EACL'97), pp. 482-489, July 1997.

Ulf Hermjakob Ph.D. Alumni ulf [at] isi edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu