SuperTagging and Full Parsing
Alexis Nasr, Owen Rambow · 2004
We investigate an approach to parsing in which lexical information is used only in a first phase, supertagging, in which lexical syntactic prop-erties are determined without building struc-ture. In the second phase, the best parse tree is determined without using lexical information. We investigate different probabilistic models for adjunction, and we show that, assuming hypothetically perfect performance in the first phase, the error rate on dependency arc attach-ment can be reduced to 2.3 % using a full chart parser. This is an improvement of about 50% over previously reported results using a simple heuristic parser. 1