Our character-based Chinese parsing model is based on the work of Zhang and Clark (2009), which is a transition-based model for lexicalized constituent parsing .

In summary, our character-based parser contains the word-based features of constituent parser presented in Zhang and Clark (2009), the word-based and shallow character-based features of joint word segmentation and POS tagging presented in Zhang and Clark (2010), and additionally the deep character-based features that encode word structure information, which are the first presented by this paper.

(a) Joint segmentation and (b) Joint constituent parsing POS tagging F—scores.

Our final performance on constituent parsing is by far the best that we are aware of for the Chinese data, and even better than some state-of-the-art models with gold segmentation.

(2007) achieves 83.45%5 in parsing accuracy on the test corpus, and our pipeline constituent parsing model achieves 83.55% with gold segmentation.

Our character-level parsing model is inspired by the work of Zhang and Clark (2009), which is a transition-based model with a beam-search decoder for word-based constituent parsing .

In addition, we propose novel features related to word structures and interactions between word segmentation, POS tagging and word-based constituent parsing .

They exploit a generative maximum entropy model for character-based constituent parsing , and find that POS information is very useful for Chinese word segmentation, but high-level syntactic information seems to have little effect on segmentation.

Shift-reduce dependency parsers give comparable accuracies to their chart-based counterparts, yet the best shift-reduce constituent parsers still lag behind the state-of-the-art.

Unlike dependency parsing, constituent parse trees for the same sentence can have different numbers of nodes, mainly due to the existence of unary nodes.

The best reported accuracies of transition-based constituent parsers still lag behind the state-of-the-art (Sagae and Lavie, 2006; Zhang and Clark, 2009).

Based on the information, we propose a set of novel features specifically designed for shift-reduce constituent parsing .

The idea of exploiting lexical dependency information from auto-parsed data has been explored before for dependency parsing (Chen et al., 2009) and constituent parsing (Zhu et al., 2012).