---
title: "MaltParser: A Data-Driven Parser-Generator for — Nivre 2006"
description: "Data-driven parsing promises faster development for new languages, but only when a treebank exists, and most available treebanks are small (roughly 100k…"
authors: ["Joakim Nivre", "Johan Hall", "Jens Nilsson"]
year: 2006
venue: "Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC'06)"
source: "https://aclanthology.org/L06-1084/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/nivre-2006-maltparser-data-driven-parser-generator/"
---
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# MaltParser: A Data-Driven Parser-Generator for Dependency Parsing

**Authors:** [Joakim Nivre](https://md.research.elamur.ai/free/authors/joakim-nivre/), [Johan Hall](https://md.research.elamur.ai/free/authors/johan-hall/), [Jens Nilsson](https://md.research.elamur.ai/free/authors/jens-nilsson/) · **Year:** 2006 · **Venue:** Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC'06) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/L06-1084/) · [PDF](https://aclanthology.org/L06-1084.pdf) · [OpenAlex](https://openalex.org/W1591659308)

**Citations:** 467 citations (openalex-snapshot · 2026-10-14) · [201](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Data-driven parsing promises faster development for new languages, but only when a treebank exists, and most available treebanks are small (roughly 100k tokens or fewer). The authors present MaltParser 0.3, a parser-generator that takes a dependency treebank instead of a grammar and induces a parser for that treebank's language. It implements inductive dependency parsing, which combines three components:

- a deterministic algorithm that builds the dependency graph step by step;
- a history-based feature model describing the current parser state;
- a discriminative classifier that selects the next parser action.

The core design goal is that these three components can be swapped and tuned independently. Users can choose among parsing algorithms and learning back-ends, and can write their own feature models in a compact tab-separated specification language. The paper documents functionality rather than new experiments. Evaluations reported elsewhere on Swedish, English, Czech, Danish and Bulgarian give dependency accuracy of 80–90%. That error rate is at most about 5% higher than state-of-the-art parsers, with no language-specific tuning and modest training data. This makes MaltParser a practical way to build parsers for languages with limited annotated resources.

## Key points
- Two parsing algorithms are included, each with two modes:
  - Nivre's algorithm runs in linear time, handles projective structures only, and can be run arc-eager or arc-standard.
  - Covington's algorithm runs in quadratic time and has a projective mode and a non-projective (acyclic) mode.
- The arc-eager variant of Nivre's algorithm gave the best accuracy on every language and data set tested so far.
- Two learning back-ends are supported:
  - memory-based learning (k-nearest neighbour) via TiMBL;
  - support vector machines via LIBSVM.
  
  All results published for the system so far use the memory-based learner.
- Each feature draws on one of three token attributes: the lexical form (LEX), the POS tag (POS), or the dependency label (DEP). Tokens are located relative to STACK, INPUT or CONTEXT, with further navigation via head, leftmost/rightmost child and left/right sibling functions. A baseline feature set is informally called "model 7."
- Input uses the Malt-TAB format: one token per line with form, POS tag, head and relation, and blank lines between sentences. Output can be Malt-TAB, Malt-XML or TIGER-XML.

## Interesting ideas
- **A parser built like a compiler.** Treating the treebank the way a classic parser-generator treats a grammar turns "build a parser for language X" into a routine training run. This is attractive wherever new treebanks keep appearing, for example small UD treebanks.
- **Separate algorithm, features and learner.** Keeping the three components decoupled lets each be varied and optimized without touching the others. The same architecture suits any transition-based system, such as tagging or chunking pipelines, where you want controlled ablation experiments.
- **Features as data, not code.** Each feature is written as an address path: base structure, then position in the stack or list, then linear shift, then steps up to heads, then steps to children and siblings. This makes feature engineering declarative, reproducible and easy to share as a config file. Undefined addresses simply get a null value instead of causing errors.
- **Suffixes instead of full words when data is scarce.** For small datasets, the authors suggest dropping peripheral lexical and POS features, or replacing whole word forms with suffixes (about 6 characters often works well). This is a cheap way to fight sparsity, and it is likely helpful for morphologically rich languages.
- **A simple, line-based interchange format.** A tab-separated token-per-line format with blank-line sentence breaks keeps tooling simple. Optional XML outputs then cover integration with other corpus tools.

*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: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

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

## Cited by

Cited by

201 papers of our library cite it (of the 140673 whose bibliographies we read); 17 of them have a page here, 184 not yet.

- [Evaluation of Dependency Parsers on Unbounded Dependencies](https://md.research.elamur.ai/free/papers/aclbulk-2010-nivre-evaluation-dependency-parsers-unbounded-dependencies/)
- [Parsing Arabic Dialects Revisited: New Benchmarks, Models, and Insights](https://md.research.elamur.ai/free/papers/aclbulk-2026-elshabrawy-parsing-arabic-dialects-revisited-new/)
- [Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling](https://md.research.elamur.ai/free/papers/akbik-2015-generating-high-quality-proposition-banks/)
- [Universal dependencies for Scottish Gaelic: syntax](https://md.research.elamur.ai/free/papers/batchelor-2019-universal-dependencies-scottish-gaelic-syntax/)
- [An Empirical Comparison of Parsing Methods for Stanford Dependencies](https://md.research.elamur.ai/free/papers/enud-2014-kong-empirical-comparison-parsing-methods-stanford/)
- [Exploring Difficulties in Parsing Imperatives and Questions](https://md.research.elamur.ai/free/papers/hara-2011-exploring-difficulties-parsing-imperatives-questions/)
- [I3rab: A New Arabic Dependency Treebank Based on Arabic Grammatical Theory](https://md.research.elamur.ai/free/papers/langstream-2020-halabi-i3rab-new-arabic-dependency-treebank/)
- [Hebrew Dependency Parsing: Initial Results](https://md.research.elamur.ai/free/papers/lingbulk-2009-goldberg-hebrew-dependency-parsing-initial-results/)
- [Camel Treebank: An Open Multi-genre Arabic Dependency Treebank](https://md.research.elamur.ai/free/papers/lingbulk-2022-habash-camel-treebank-open-multi-genre/)
- [Multi-Source Transfer of Delexicalized Dependency Parsers](https://md.research.elamur.ai/free/papers/mcdonald-2011-multi-source-transfer-delexicalized-dependency/)
- [A Fast and Accurate Dependency Parser using Neural Networks](https://md.research.elamur.ai/free/papers/missing-2014-chen-fast-accurate-dependency-parser-neural/)
- [Labeled Pseudo-Projective Dependency Parsing with Support Vector Machines](https://md.research.elamur.ai/free/papers/nivre-2006-labeled-pseudo-projective-dependency-parsing/)
- [Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment](https://md.research.elamur.ai/free/papers/tacl-2014-belinkov-exploring-compositional-architectures-word-vector/)
- [Universal Dependencies for Arabic](https://md.research.elamur.ai/free/papers/ud-2017-taji-universal-dependencies-arabic/)
- [Dependency Parsing for Spoken Dialog Systems](https://md.research.elamur.ai/free/papers/ud-2019-davidson-dependency-parsing-spoken-dialog-systems/)
- [Nucleus Composition in Transition-based Dependency Parsing](https://md.research.elamur.ai/free/papers/ud-2022-nivre-nucleus-composition-transition-based-dependency/)
- [Boosting Transition-based AMR Parsing with Refined Actions and Auxiliary Analyzers](https://md.research.elamur.ai/free/papers/wang-2015-boosting-transition-based-amr-parsing/)

## Cite

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

```bibtex
@inproceedings{nivre2006maltparser,
  title = {{MaltParser: A Data-Driven Parser-Generator for Dependency Parsing}},
  author = {Nivre, Joakim and Hall, Johan and Nilsson, Jens},
  booktitle = {Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC'06)},
  year = {2006},
  doi = {10.63317/2c4iw9v6wxqe},
  url = {https://doi.org/10.63317/2c4iw9v6wxqe}
}
```

- [← previous MAGEAD: A Morphological Analyzer and Generator for the Arabic Dialects](https://md.research.elamur.ai/free/papers/aclbulk-2006-habash-magead-morphological-analyzer-generator-arabic/)
- [next → Manual and Automatic Evaluation of Machine Translation between European Languages](https://md.research.elamur.ai/free/papers/koehn-2006-manual-automatic-evaluation-machine-translation/)
- [↑ back to papers of 2006](https://md.research.elamur.ai/free/papers/2006/)

## References

13 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. E. Black, F. Jelinek, J. Lafferty, D. Magerman, R. Mercer, and S. Roukos. 1992. Towards history-based grammars: Using richer models for probabilistic parsing. In Proceedings of the 5th DARPA Speech and Natural Language Workshop, pages 31–37. [find](https://scholar.google.com/scholar?q=E%2E+Black%2C+F%2E+Jelinek%2C+J%2E+Lafferty%2C+D%2E+Magerman%2C+R%2E+Mercer%2C+and+S%2E+Roukos%2E+1992%2E+Towards+history%2Dbased+grammars%3A+Using+richer+models+for+probabilistic+parsing%2E+In+Proceedings+of+the+5th+DARPA+Speech+an)
2. Chih-Chung Chang and Chih-Jen Lin, 2001. LIBSVM: a library for support vector machines. Software available at [http://www.csie.ntu.edu.tw/∼cjlin/libsvm](http://www.csie.ntu.edu.tw/∼cjlin/libsvm). [↗](http://www.csie.ntu.edu.tw/∼cjlin/libsvm)
3. Michael Collins. 1999. Head-Driven Statistical Models for Natural Language Parsing. Ph.D. thesis, University of Pennsylvania. [find](https://scholar.google.com/scholar?q=Michael+Collins%2E+1999%2E+Head%2DDriven+Statistical+Models+for+Natural+Language+Parsing%2E+Ph%2ED%2E+thesis%2C+University+of+Pennsylvania%2E)
4. Michael A. Covington. 2001. A fundamental algorithm for dependency parsing. In Proceedings of the 39th Annual ACM Southeast Conference, pages 95–102. [find](https://scholar.google.com/scholar?q=Michael+A%2E+Covington%2E+2001%2E+A+fundamental+algorithm+for+dependency+parsing%2E+In+Proceedings+of+the+39th+Annual+ACM+Southeast+Conference%2C+pages+95%E2%80%93102%2E)
5. Walter Daelemans and Antal Van den Bosch. 2005. Memory-Based Language Processing. Cambridge University Press. [find](https://scholar.google.com/scholar?q=Walter+Daelemans+and+Antal+Van+den+Bosch%2E+2005%2E+Memory%2DBased+Language+Processing%2E+Cambridge+University+Press%2E)
6. David M. Magerman. 1995. Statistical decision-tree models for parsing. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (ACL), pages 276–283. [find](https://scholar.google.com/scholar?q=David+M%2E+Magerman%2E+1995%2E+Statistical+decision%2Dtree+models+for+parsing%2E+In+Proceedings+of+the+33rd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28ACL%29%2C+pages+276%E2%80%93283%2E)
7. Joakim Nivre and Johan Hall. 2005. MaltParser: A language-independent system for data-driven dependency parsing. In Proceedings of the Fourth Workshop on Treebanks and Linguistic Theories (TLT). [find](https://scholar.google.com/scholar?q=Joakim+Nivre+and+Johan+Hall%2E+2005%2E+MaltParser%3A+A+language%2Dindependent+system+for+data%2Ddriven+dependency+parsing%2E+In+Proceedings+of+the+Fourth+Workshop+on+Treebanks+and+Linguistic+Theories+%28TLT%29%2E)
8. Joakim Nivre, Johan Hall, and Jens Nilsson. 2004. Memory-based dependency parsing. In Hwee Tou Ng and Ellen Riloff, editors, Proceedings of the 8th Conference on Computational Natural Language Learning (CoNLL), pages 49–56. [find](https://scholar.google.com/scholar?q=Joakim+Nivre%2C+Johan+Hall%2C+and+Jens+Nilsson%2E+2004%2E+Memory%2Dbased+dependency+parsing%2E+In+Hwee+Tou+Ng+and+Ellen+Riloff%2C+editors%2C+Proceedings+of+the+8th+Conference+on+Computational+Natural+Language+Learnin) [our card](https://md.research.elamur.ai/free/papers/nivre-2004-memory-based-dependency-parsing/)
9. Joakim Nivre. 2003. An efficient algorithm for projective dependency parsing. In Gertjan Van Noord, editor, Proceedings of the 8th International Workshop on Parsing Technologies (IWPT), pages 149–160. [find](https://scholar.google.com/scholar?q=Joakim+Nivre%2E+2003%2E+An+efficient+algorithm+for+projective+dependency+parsing%2E+In+Gertjan+Van+Noord%2C+editor%2C+Proceedings+of+the+8th+International+Workshop+on+Parsing+Technologies+%28IWPT%29%2C+pages+149%E2%80%93160%2E) [our card](https://md.research.elamur.ai/free/papers/parsers-2003-nivre-efficient-algorithm-projective-dependency-parsing/)
10. Joakim Nivre. 2004. Incrementality in deterministic dependency parsing. In Frank Keller, Stephen Clark, Matthew Crocker, and Mark Steedman, editors, Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together (ACL), pages 50– 57. [find](https://scholar.google.com/scholar?q=Joakim+Nivre%2E+2004%2E+Incrementality+in+deterministic+dependency+parsing%2E+In+Frank+Keller%2C+Stephen+Clark%2C+Matthew+Crocker%2C+and+Mark+Steedman%2C+editors%2C+Proceedings+of+the+Workshop+on+Incremental+Parsing%3A) [our card](https://md.research.elamur.ai/free/papers/nivre-2004-incrementality-deterministic-dependency-parsing/)
11. Joakim Nivre. 2005. Inductive Dependency Parsing of Natural Language Text. Ph.D. thesis, Växjö University. [find](https://scholar.google.com/scholar?q=Joakim+Nivre%2E+2005%2E+Inductive+Dependency+Parsing+of+Natural+Language+Text%2E+Ph%2ED%2E+thesis%2C+Va%CC%88xjo%CC%88+University%2E)
12. Adwait Ratnaparkhi. 1997. A linear observed time statistical parser based on maximum entropy models. In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1– 10. [find](https://scholar.google.com/scholar?q=Adwait+Ratnaparkhi%2E+1997%2E+A+linear+observed+time+statistical+parser+based+on+maximum+entropy+models%2E+In+Proceedings+of+the+Second+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29)
13. Hiroyasu Yamada and Yuji Matsumoto. 2003. Statistical dependency analysis with support vector machines. In Gertjan Van Noord, editor, Proceedings of the 8th International Workshop on Parsing Technologies (IWPT), pages 195–206. [find](https://scholar.google.com/scholar?q=Hiroyasu+Yamada+and+Yuji+Matsumoto%2E+2003%2E+Statistical+dependency+analysis+with+support+vector+machines%2E+In+Gertjan+Van+Noord%2C+editor%2C+Proceedings+of+the+8th+International+Workshop+on+Parsing+Technolog) [our card](https://md.research.elamur.ai/free/papers/parsers-2003-yamada-statistical-dependency-analysis-support-vector/)