Abstract

We present two methods for incorporating detailed features in a Spanish parser, building on a baseline model that is a lexicalized 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 information with an approach that makes use of general structural features.

Citations