Hybrid ways to improve domain independence in an ML dependency parser

This paper reports a hybridization experiment, where a baseline ML dependency parser, LingPars, was allowed access to Constraint Grammar analyses provided by a rulebased parser (EngGram) for the same data. Descriptive compatibility issues and their influence on performance are discussed. The hybrid system performed considerably better than its ML baseline, and proved more robust than the latter in the domain adaptation task, where it was the bestscoring system in the open class for the chemical test data, and the best overall system for the CHILDES test data.