---
title: "Integrating Graph-Based and Transition-Based — Nivre 2008"
description: "The paper tackles a known tension in data-driven dependency parsing."
authors: ["Joakim Nivre", "Ryan McDonald"]
year: 2008
venue: "Proceedings of ACL-08: HLT"
source: "https://aclanthology.org/P08-1108/"
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/nivre-2008-integrating-graph-based-transition-based/"
---
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# Integrating Graph-Based and Transition-Based Dependency Parsers

**Authors:** [Joakim Nivre](https://md.research.elamur.ai/free/en/authors/joakim-nivre/), [Ryan McDonald](https://md.research.elamur.ai/free/en/authors/ryan-mcdonald/) · **Year:** 2008 · **Venue:** Proceedings of ACL-08: HLT · **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/P08-1108/) · [PDF](https://aclanthology.org/P08-1108.pdf) · [OpenAlex](https://openalex.org/W2108161779)

**Citations:** 227 citations (openalex-snapshot · 2026-10-14) · [127](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper tackles a known tension in data-driven dependency parsing. Graph-based parsers rely on global training and exact search but can only use features over one or two arcs. Transition-based parsers make greedy, locally trained decisions but can condition on everything built so far. Earlier error analysis showed the two families fail in different, complementary places. The authors therefore combine them at training time rather than only at parsing time. One parser, the guide, produces a full parse, and features describing that parse are added to the feature vector of the other parser, the base, which then learns when to rely on them (a form of classifier stacking). They apply this in both directions using MSTParser and MaltParser on the CoNLL-X shared task data sets. Both guided parsers consistently beat their unguided versions, giving a significant gain over the previous state of the art. MST guided by Malt benefits most. The approach matters because it only requires defining new features, yet it exploits complementary model biases in a principled way.

## Key points
- The two systems are MSTParser and MaltParser:
  - **MSTParser** (McDonald et al., 2006) scores pairs of arcs, uses near-exhaustive unlabeled search plus a separate arc labeler, and is trained online with MIRA.
  - **MaltParser** (Nivre et al., 2006) uses the linear-time Nivre (2003) transition system with SVM-learned transition scores.
- The earlier error analysis behind the method (McDonald and Nivre, 2007) found clear differences:
  - Malt is weaker on long arcs and arcs near the root, but stronger on short, deeply embedded arcs.
  - Malt is better on noun and pronoun dependents.
  - MST is better on verbs, adjectives, adverbs, adpositions and conjunctions.
- The guide features (Table 1) are simple questions about the guide parse:
  - For MST_Malt: whether an arc (i, j), with or without a given label, is present in Malt's output, and which label Malt gives to a node.
  - For Malt_MST: whether MST links the top of the stack and the front of the buffer, plus MST's head direction and labels for those tokens.
- Evaluation is on the CoNLL-X shared task data sets. Both guided models improve, and MST_Malt ends up significantly more accurate than Malt_MST.
- Related combinations each reported about 1.7 points on the Penn Treebank:
  - Sagae and Lavie (2006): parse-time voting with four parsers.
  - McDonald (2006): MSTParser using features from the Collins and Charniak parsers.

## Interesting ideas
- **Combine at learning time, not just by voting.** Feeding one model's single best output to another as extra features lets the second model learn context-dependent trust in the first. Only two systems are needed, unlike voting ensembles that require at least three.
- **Pick partners by their error profiles.** The pairing was motivated by complementary error distributions, not chosen ad hoc. Running an error breakdown by arc length, depth and POS before building an ensemble is a reusable recipe for any pair of structured predictors.
- **Global inference spreads local gains.** The base model benefits more from guidance when its inference is global, because fixing one decision type helps others, while greedy decoders lose part of the gain to error propagation. This is useful when deciding which model in a pipeline should receive the auxiliary signals.
- **The learner affects how much guide features are used.** With a batch, regularized learner such as an SVM, guide features may get small weights if the original features already separate the training data. An online learner such as MIRA tends to latch onto them early and weight them heavily. Inspecting learned weights is a cheap way to check whether stacked features are actually used.
- **Stacking is a cheap alternative to re-ranking.** Only the guide's top parse is consumed, and the base model is not forced to follow it. This keeps the cost far below n-best re-ranking, which matters for low-resource or production settings.

*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/)

## Cited by

Cited by

127 papers of our library cite it (of the 140884 whose bibliographies we read); 10 of them have a page here, 117 not yet.

- [A Dependency Parser for Tweets](https://md.research.elamur.ai/free/en/papers/aclbulk-2014-kong-dependency-parser-tweets/)
- [Top Accuracy and Fast Dependency Parsing is not a Contradiction](https://md.research.elamur.ai/free/en/papers/bohnet-2010-top-accuracy-fast-dependency-parsing/)
- [Correcting Dependency Annotation Errors](https://md.research.elamur.ai/free/en/papers/dickinson-2009-correcting-dependency-annotation-errors/)
- [An Empirical Comparison of Parsing Methods for Stanford Dependencies](https://md.research.elamur.ai/free/en/papers/enud-2014-kong-empirical-comparison-parsing-methods-stanford/)
- [Frame-Semantic Role Labeling with Heterogeneous Annotations](https://md.research.elamur.ai/free/en/papers/kshirsagar-2015-frame-semantic-role-labeling-heterogeneous/)
- [A Non-Monotonic Arc-Eager Transition System for Dependency Parsing](https://md.research.elamur.ai/free/en/papers/lingbulk-2013-honnibal-non-monotonic-arc-eager-transition/)
- [Non-Projective Dependency Parsing in Expected Linear Time](https://md.research.elamur.ai/free/en/papers/nivre-2009-non-projective-dependency-parsing-expected/)
- [Integrating Graph-Based and Transition-Based Dependency Parsers in the Deep Contextualized Era](https://md.research.elamur.ai/free/en/papers/ud-2020-falenska-integrating-graph-based-transition-based/)
- [A Tale of Two Parsers: Investigating and Combining Graph-based and Transition-based Dependency Parsing](https://md.research.elamur.ai/free/en/papers/zhang-2008-tale-two-parsers-investigating-combining/)
- [Semantic Dependency Parsing of NomBank and PropBank: An Efficient Integrated Approach via a Large-scale Feature Selection](https://md.research.elamur.ai/free/en/papers/zhao-2009-semantic-dependency-parsing-nombank-propbank/)

## Cite

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

```bibtex
@inproceedings{nivre2008integrating,
  title = {{Integrating Graph-Based and Transition-Based Dependency Parsers}},
  author = {Nivre, Joakim and McDonald, Ryan},
  booktitle = {Proceedings of ACL-08: HLT},
  year = {2008},
  url = {https://aclanthology.org/P08-1108/}
}
```

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- [↑ back to papers of 2008](https://md.research.elamur.ai/free/en/papers/2008/)

## References

33 references, 16 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Giuseppe Attardi. 2006. Experiments with a multilanguage non-projective dependency parser. In Proceedings of CoNLL, pages 166–170. [find](https://scholar.google.com/scholar?q=Giuseppe+Attardi%2E+2006%2E+Experiments+with+a+multilanguage+non%2Dprojective+dependency+parser%2E+In+Proceedings+of+CoNLL%2C+pages+166%E2%80%93170%2E) [our card](https://md.research.elamur.ai/free/en/papers/attardi-2006-experiments-multilanguage-non-projective-dependency/)
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5. Michael Collins. 2000. Discriminative reranking for natural language parsing. In Proceedings of ICML, pages 175–182. [find](https://scholar.google.com/scholar?q=Michael+Collins%2E+2000%2E+Discriminative+reranking+for+natural+language+parsing%2E+In+Proceedings+of+ICML%2C+pages+175%E2%80%93182%2E) [our card](https://md.research.elamur.ai/free/en/papers/collins-2000-discriminative-reranking-natural-language-parsing/)
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12. Kristy Hollingshead and Brian Roark. 2007. Pipeline iteration. In Proceedings of ACL, pages 952–959. [find](https://scholar.google.com/scholar?q=Kristy+Hollingshead+and+Brian+Roark%2E+2007%2E+Pipeline+iteration%2E+In+Proceedings+of+ACL%2C+pages+952%E2%80%93959%2E)
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14. Ryan McDonald and Joakim Nivre. 2007. Characterizing the errors of data-driven dependency parsing models. In Proceedings of EMNLP-CoNLL, pages 122– 131. [find](https://scholar.google.com/scholar?q=Ryan+McDonald+and+Joakim+Nivre%2E+2007%2E+Characterizing+the+errors+of+data%2Ddriven+dependency+parsing+models%2E+In+Proceedings+of+EMNLP%2DCoNLL%2C+pages+122%E2%80%93+131%2E) [our card](https://md.research.elamur.ai/free/en/papers/missing4-2007-mcdonald-characterizing-errors-data-driven-dependency/)
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17. Ryan McDonald, Fernando Pereira, Kiril Ribarov, and Jan Hajič. 2005b. Non-projective dependency parsing using spanning tree algorithms. In Proceedings of HLT/EMNLP, pages 523–530. [find](https://scholar.google.com/scholar?q=Ryan+McDonald%2C+Fernando+Pereira%2C+Kiril+Ribarov%2C+and+Jan+Hajic%CC%8C%2E+2005b%2E+Non%2Dprojective+dependency+parsing+using+spanning+tree+algorithms%2E+In+Proceedings+of+HLT%2FEMNLP%2C+pages+523%E2%80%93530%2E) [our card](https://md.research.elamur.ai/free/en/papers/narrative-2005-mcdonald-non-projective-dependency-parsing-spanning/)
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