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Supertagging and full parsing

By Alexis Nasr and Owen Rambow


We investigate an approach to parsing in which lexical information is used only in a first phase, supertagging, in which lexical syntactic properties are determined without building structure. 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 attachment 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.

Year: 2004
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