---
title: "Transition-Based Parsing of the Chinese Treebank using a Global Discriminative Model — Zhang 2009"
description: "They replace the local decisions with one linear model that scores whole action sequences. 22 references, cited by 54 papers here."
authors: ["Yue Zhang", "Stephen Clark"]
year: 2009
venue: "Proceedings of the 11th International Conference on Parsing Technologies (IWPT'09)"
source: "https://aclanthology.org/W09-3825/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2009-transition-based-parsing-chinese-treebank/"
---
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# Transition-Based Parsing of the Chinese Treebank using a Global Discriminative Model

**Authors:** [Yue Zhang](https://md.research.elamur.ai/free/en/authors/yue-zhang/) and [Stephen Clark](https://md.research.elamur.ai/free/en/authors/stephen-clark/) · **Year:** 2009 · **Venue:** Proceedings of the 11th International Conference on Parsing Technologies (IWPT'09) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/W09-3825/) · [PDF](https://aclanthology.org/W09-3825.pdf) · [OpenAlex](https://openalex.org/W2052816566)

**Citations:** 97 citations (OpenAlex, 2026-10-14) · [54](https://md.research.elamur.ai/free/en/papers/zhang-2009-transition-based-parsing-chinese-treebank/cited-by/) in our library, of the 140,884 papers whose bibliographies we read

## Summary
The paper targets constituent parsing of Chinese with the Chinese Treebank (CTB). The best earlier transition-based system, Wang et al. (2006), relied on a greedy shift-reduce parser with a local SVM classifier at each step. Zhang and Clark keep the same shift-reduce transitions over binarized, head-lexicalized trees. They replace the local decisions with one linear model that scores whole action sequences. Weights are learned with an averaged generalized perceptron plus early update, and decoding uses beam search instead of greedy choice. Parsing still runs in linear time, and competing hypotheses in the beam can recover from early mistakes.

The authors also make three practical changes. They add a TERMINATE action so that unary-branching roots can be built. They impose a set of constraints that guarantees every output is a valid tree. They hand-craft combined features for the linear model.

On CTB 2 the parser matches the Parseval F-score of the state of the art. On the larger CTB 5, its output converted to dependencies gives the best dependency scores reported so far. Accuracy falls noticeably when POS tags are predicted automatically rather than taken from gold annotation.

## Key points
- **Method:** shift-reduce constituent parsing with a global discriminative model. Training uses an averaged perceptron with early update (Collins and Roark, 2004), and decoding is beam search that keeps the B best states.
- **New TERMINATE action:** lets unary reduces apply at the root. This matters for 14 of the 3475 CTB 2 training sentences, which Wang et al.'s parser cannot produce correctly.
- **Binarization:** a reversible, head-driven scheme introduces temporary "X*" labels. Restrictions on legal actions always yield well-formed trees, including a cap of three consecutive unary reduces.
- **Features:** context windows come from Wang et al. (stack nodes S0–S3 and queue words N0–N3). Unigrams are combined by hand into bigram and trigram templates, alongside bracket and separator (punctuation) features.
- **Results and resource:** accuracy is comparable to the best systems on CTB 2, and dependency accuracy on CTB 5 is the highest reported. The code is released as part of ZPar (sourceforge.net/projects/zpar, make target `chinese.conparser`).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [🌳 Dependency parsing](https://md.research.elamur.ai/free/en/topics/dependency-parsing/) · [🗂️ Treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/)

## Institutions

- [University of Oxford](https://md.research.elamur.ai/free/en/universities/university-of-oxford/) — University, Oxford, United Kingdom · 39 works · 153,213 citations
- [University of Cambridge](https://md.research.elamur.ai/free/en/universities/university-of-cambridge/) — University, Cambridge, United Kingdom · 119 works · 110,950 citations

## Cite

Cite as: Yue Zhang and Stephen Clark. 2009. Transition-Based Parsing of the Chinese Treebank using a Global Discriminative Model. Proceedings of the 11th International Conference on Parsing Technologies (IWPT'09).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2009transition,
  title = {{Transition-Based Parsing of the Chinese Treebank using a Global Discriminative Model}},
  author = {Yue Zhang and Stephen Clark},
  booktitle = {Proceedings of the 11th International Conference on Parsing Technologies (IWPT'09)},
  year = {2009},
  doi = {10.3115/1697236.1697267},
  url = {https://doi.org/10.3115/1697236.1697267}
}
```

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## References

22 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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