We propose Symbol-Refined Tree Substitution Grammars (SR-TSGs) for syntactic parsing .

Syntactic parsing has played a central role in natural language processing.

tree fragments and symbol refinement work complementarily for syntactic parsing .

In this paper, we propose Symbol-Refined Tree Substitution Grammars (SR-TSGs) for syntactic parsing .

In this section, we propose Symbol-Refined Tree Substitution Grammars (SR-TSGs) for syntactic parsing .

In this work, we further extend this line of exploration and propose a novel but simple approach, which utilizes a ranking model based on word order precedence in the target language to reposition nodes in the syntactic parse tree of a source sentence.

The ranking model is automatically derived from word aligned parallel data with a syntactic parser for source language based on both lexical and syntactical features.

The most notable solution to this problem is adopting syntaX-based SMT models, especially methods making use of source side syntactic parse trees.

The other is called syntax pre-reordering — an approach that re-positions source words to approximate target language word order as much as possible based on the features from source syntactic parse trees.

In this paper, we continue this line of work and address the problem of word reordering based on source syntactic parse trees for SMT.

Table 1: Argument clustering performance with gold argument identification and gold syntactic parses on CoNLL 2008 shared-task dataset.

We report the results using gold argument identification and gold syntactic parses in order to focus the evaluation on the argument labeling stage and to minimize the noise due to automatic syntactic annotations.

Table 2: Results on CONLL 2009 with automatic argument identification and automatic syntactic parses .

Learning in the context of multiple languages simultaneously has been shown to be beneficial to a number of NLP tasks from morphological analysis to syntactic parsing (Kuhn, 2004; Snyder and Barzilay, 2010; McDonald et al., 2011).