Our re-ranking approach , like the approach to parse re-ranking of Collins ( 2000 ) , employs a simpler model -- a local semantic role labeling algorithm -- as a first pass to generate a set of n likely complete assignments of labels to all parse tree nodes .
discriminative reranking for natural language parsing
Michael Collins
To avoid overfitting we encode only a small set of linguistically motivated dependencies in features over sets of the random variables . Our re-ranking approach , like the approach to parse re-ranking of Collins ( 2000 ) , employs a simpler model -- a local semantic role labeling algorithm -- as a first pass to generate a set of n likely complete assignments of labels to all parse tree nodes . The joint model is restricted to these n assignments and does not have to search the exponentially large space of all possible joint labelings .
Our re-ranking approach , like the approach to parse re-ranking of @@CITATION , employs a simpler model -- a local semantic role labeling algorithm -- as a first pass to generate a set of n likely complete assignments of labels to all parse tree nodes .