Multilingual dependency-based syntactic and semantic parsing

Abstract

Our CoNLL 2009 Shared Task system includes three cascaded components: syntactic parsing, predicate classification, and semantic role labeling.A pseudo-projective high-order graph-based model is used in our syntactic dependency parser.A support vector machine (SVM) model is used to classify predicate senses.Semantic role labeling is achieved using maximum entropy (MaxEnt) model based semantic role classification and integer linear programming (ILP) based post inference.Finally, we win the first place in the joint task, including both the closed and open challenges. 1 System Architecture Our CoNLL 2009 Shared Task (Hajič et al., 2009): multilingual syntactic and semantic dependencies system includes three cascaded components: syntactic parsing, predicate classification, and semantic role labeling. Syntactic Dependency ParsingWe extend our CoNLL 2008 graph-based model (Che et al., 2008) in four ways:1. We use bigram features to choose multiple possible syntactic labels for one arc, and decide the optimal label during decoding.2. We extend the model with sibling features (Mc-Donald, 2006).3. We extend the model with grandchildren features.Rather than only using the left-most and rightmost grandchildren as Carreras (2007) and Johansson and Nugues (2008) did, we use all left and right grandchildren in our model.4. We adopt the pseudo-projective approach introduced in (Nivre and Nilsson, 2005) to handle the non-projective languages including Czech, German and English. Syntactic Label Determining