Evaluation on the Penn Chinese Treebank indicates that a converted dependency treebank helps constituency parsing and the use of unlabeled data by self-training further increases parsing f-score to 85.2%, resulting in 6% error reduction over the previous best result.

Moreover, experimental results on the Penn Chinese Treebank indicate that a converted dependency treebank helps constituency parsing , and it is better to exploit probability information produced by the parser through score interpolation than to prune low quality trees for the use of the converted treebank.

We first train a constituency parser on CPS

(1999) performed statistical constituency parsing of Czech on a treebank that was converted from the Prague Dependency Treebank under the guidance of conversion rules and heuristic rules, e.g., one level of projection for any category, minimal projection for any dependents, and fixed position of attachment.

We investigate the task of unsupervised constituency parsing from bilingual parallel corpora.

In this paper we investigate the task of unsupervised constituency parsing when bilingual parallel text is available.

While PCFGs perform poorly on this task, the CCM model (Klein and Manning, 2002) has achieved large gains in performance and is among the state-of-the-art probabilistic models for unsupervised constituency parsing .