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# Algorithms for Deterministic Incremental Dependency Parsing

**Authors:** [Joakim Nivre](https://md.mova.elamur.ai/free/en/docs/papers/authors/joakim-nivre/) · **Year:** 2008 · **Venue:** Computational Linguistics, vol. 34, no. 4 **Link:** [aclanthology.org/J08-4003](https://aclanthology.org/J08-4003/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**435** citations](https://openalex.org/works?filter=cites:W2105847779) openalex · 2026-10-18[**208** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/narrative-2008-nivre-algorithms-deterministic-incremental-dependency-parsing/) of the 140673 papers whose bibliographies we read[Computational Linguistics](https://aclanthology.org/volumes/J08-4/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/J08-4003/)
- [PDF](https://aclanthology.org/J08-4003.pdf)
- [DOI 10.1162/coli.07-056-r1-07-027](https://doi.org/10.1162/coli.07-056-r1-07-027)
- [OpenAlex](https://openalex.org/W2105847779)

**Authors (1)**

1. [Nivre, Joakim](https://openalex.org/A5077063699) h 60 · 16290 citations · 335 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper sets out deterministic incremental dependency parsing as transition systems and analyses four algorithms: two stack-based projective ones (arc-eager and arc-standard) and two list-based ones (a projective and a non-projective variant). For each it gives correctness and complexity proofs. In experiments with SVM classifiers on 13 languages from CoNLL-X, all four give competitive accuracy. The non-projective list-based parser is the most accurate on languages with many non-projective arcs, while pseudo-projective parsing brings the projective algorithms close to it. The average LAS across the algorithms differs by only 0.58 points, and the stack-based ones run in linear time, though the projective list-based one is about as fast in practice.

## Ideas

- I1. en/src/parse.rs: add a non-projective list-based (Covington-style) option next to arc-standard and measure LAS and time per sentence on EWT dev and on GUM test (test only, NC licence), separately for the non-projective sentences.
- I2. en/src/convert.rs and en/src/parse.rs: pseudo-projective lifting (encode the lifted label in the dependency relation, then restore it after parsing) and measure LAS on EWT dev, to see whether the projective parser can match the non-projective one at no cost.
- I3. Report the spread of LAS across parsers in `en parse-eval` output on one data set, as this paper does, so that a difference of a few tenths of a point is read against the noise of the parser choice.

## For Mova

The ideas touch `en/src/parse.rs`, the converters in `en/src/convert.rs`, and the report of `en parse-eval`. The chapters are `languages/en/20-syntax-overview` and `languages/en/29-ellipsis-word-order-information-structure`, where word order and non-projective arcs matter. Measure first I3, which is a reporting change, and then I2, which tests whether the projective parser can absorb the few non-projective English arcs.

Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1162/coli.07-056-r1-07-027](https://doi.org/10.1162/coli.07-056-r1-07-027)

```
@article{nivre2008algorithms,
  title = {{Algorithms for Deterministic Incremental Dependency Parsing}},
  author = {Nivre, Joakim},
  journal = {Computational Linguistics},
  year = {2008},
  doi = {10.1162/coli.07-056-r1-07-027},
  url = {https://doi.org/10.1162/coli.07-056-r1-07-027}
}
```

- [← previous Online Large-Margin Training of Dependency Parsers](https://md.mova.elamur.ai/free/en/docs/papers/narrative-2005-mcdonald-online-large-margin-training-dependency/)
- [next → Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://md.mova.elamur.ai/free/en/docs/papers/narrative-2016-kiperwasser-simple-accurate-dependency-parsing-bidirectional/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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