A second-order joint eisner model for syntactic and semantic dependency parsing
Xavier Lluís, Stefan Bott, Lluı́s Màrquez · 2009
We present a system developed for the CoNLL-2009 Shared Task (Hajič et al., 2009). We extend the Carreras (2007) parser to jointly annotate syntactic and semantic dependencies. This state-of-the-art parser factorizes the built tree in second-order factors. We include semantic dependencies in the factors and extend their score function to combine syntactic and semantic scores. The parser is coupled with an on-line averaged perceptron (Collins, 2002) as the learning method. Our averaged results for all seven languages are 71.49 macro F1, 79.11 LAS and 63.06 semantic F1.