Paper: Morphology And Reranking For The Statistical Parsing Of Spanish

ACL ID H05-1100
Title Morphology And Reranking For The Statistical Parsing Of Spanish
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

We present two methods for incorporat- ing detailed features in a Spanish parser, building on a baseline model that is a lex- icalized PCFG. The first method exploits Spanish morphology, and achieves an F1 constituency score of 83.6%. This is an improvement over 81.2% accuracy for the baseline, which makes little or no use of morphological information. The second model uses a reranking approach to add arbitrary global features of parse trees to the morphological model. The reranking model reaches 85.1% F1 accuracy on the Spanish parsing task. The resulting model for Spanish parsing combines an approach that specifically targets morphological in- formation with an approach that makes use of general structural features.