Data-driven parsers rely on recommendations from parse models, which are generated from a set of training data using a machine learning classifier, to perform parse operations. However, in some cases a parse model cannot recommend a parse action to a parser unless it learns from the training data what parse action(s) to take in every possible situation. Therefore, it will be hard for a parser to make an informed decision as to what parse operation to perform when a parse model recommends no/several parse actions to a parser. Here we examine the effect of various deterministic choices on a datadriven parser when it is presented with no/several recommendation from a parse model.
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