---
title: "Dependency Parsing and Domain Adaptation with — Sagae 2007"
description: "Sagae and Tsujii present the system they entered in the two tracks of the CoNLL 2007 shared task: multilingual dependency parsing across ten treebanks…"
authors: ["Kenji Sagae", "Jun'ichi Tsujii"]
year: 2007
venue: "Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)"
source: "https://aclanthology.org/D07-1111/"
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/sagae-2007-dependency-parsing-domain-adaptation-lr/"
---
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# Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles

**Authors:** [Kenji Sagae](https://md.research.elamur.ai/free/en/authors/kenji-sagae/), [Jun'ichi Tsujii](https://md.research.elamur.ai/free/en/authors/jun-ichi-tsujii/) · **Year:** 2007 · **Venue:** Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) · **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/D07-1111/) · [PDF](https://aclanthology.org/D07-1111.pdf) · [OpenAlex](https://openalex.org/W2124772738)

**Citations:** 230 citations (OpenAlex, 2026-10-14) · [81](https://md.research.elamur.ai/free/en/papers/sagae-2007-dependency-parsing-domain-adaptation-lr/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
Sagae and Tsujii present the system they entered in the two tracks of the CoNLL 2007 shared task: multilingual dependency parsing across ten treebanks, and domain adaptation from Wall Street Journal (Penn Treebank) dependencies to biomedical and chemical text. The core parser adapts the classic LR shift-reduce algorithm. Instead of an LR table derived from a grammar, a classifier chooses actions using features from the top stack items and upcoming input tokens. A best-first search over action probabilities makes it a probabilistic, GLR-like parser, and pseudo-projective transformations handle non-projective trees.

For the multilingual track, three differently built models per language are merged by maximum-spanning-tree voting. For domain adaptation, two dissimilar models parse unlabeled target-domain text, and only sentences where they produce exactly the same analysis are added to the training data before retraining.

The system ranked first in domain adaptation (LAS 81.06), beating even the open-track results, and came within 0.43 points of the best multilingual average (79.89). This shows that simple ensembles and agreement-based self-training are highly effective.

## Key points
- **Multilingual ensemble.** Each language gets three LR models: forward MaxEnt, backward MaxEnt (input reversed), and an SVM model with a degree-2 polynomial kernel. The SVM model ran deterministically because Platt-scaled probabilities brought no gain. The three outputs are combined by unweighted MST voting after deprojectivization.
- **Shared settings.** All ten languages used identical features and meta-parameters. Non-projectivity was handled with Nivre and Nilsson's PATH scheme, chosen because it keeps the number of dependency label types from growing too much.
- **Agreement as a quality signal.** On development data, sentences where the forward MaxEnt and backward SVM models agreed reached about 90+ LAS, while each model alone scored roughly 78–79.
- **Domain adaptation gains.** About 200k words of agreed-upon target-domain parses were added to the training set. This raised development LAS to 82.1, and the final test scores were LAS 81.06 and UAS 83.42 (closed track). The multilingual ensemble, without any unlabeled data, reached 81.9 on the same development set.
- **Turkish and parsing direction.** In Turkish, over 80% of heads lie to the right of their dependents. Backward models clearly outperformed forward ones there, and adding a backward SVM model after submission raised the development combination score from 75.8 to 77.9.

## Interesting ideas
- **Cheap diversity for ensembles.** Variety can come from the same algorithm simply by changing the learner or reversing the input sentence. This makes ensembling practical whenever only one parser implementation is available.
- **Agreement-filtered self-training.** Automatically parsed sentences are kept only when two dissimilar models produce identical trees. This yields high-precision pseudo-labels for a new domain without any annotation, and the idea could transfer to low-resource languages or other structured prediction tasks.
- **Matching direction to head position.** Checking whether heads in a treebank tend to sit left or right of their dependents can guide the choice of parsing direction. Strongly head-final languages benefit from processing that builds structure early instead of waiting until most of the input is consumed.
- **Classifier in place of an LR table.** A learned classifier replaces the grammar-derived table, and a best-first heap over action-probability products upgrades a greedy shift-reduce parser to a probabilistic search with little extra machinery.
- **A single ensemble for multiple goals.** The same ensemble that boosts multilingual accuracy also adapted well across domains on its own. Ensembling is therefore a reasonable robustness baseline before trying dedicated adaptation methods.

*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/) · [Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/)

## Institutions

- [The University of Tokyo](https://md.research.elamur.ai/free/en/universities/the-university-of-tokyo/) — University, Tokyo, Japan · 31 works · 7 442 citations

## Cite

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

```bibtex
@inproceedings{sagae2007dependency,
  title = {{Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles}},
  author = {Sagae, Kenji and Tsujii, Jun'ichi},
  booktitle = {Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)},
  year = {2007},
  url = {https://aclanthology.org/D07-1111/}
}
```

- [← previous Chinese Syntactic Reordering for Statistical Machine Translation](https://md.research.elamur.ai/free/en/papers/wang-2007-chinese-syntactic-reordering-statistical-machine/)
- [next → Improving Translation Quality by Discarding Most of the Phrasetable](https://md.research.elamur.ai/free/en/papers/johnson-2007-improving-translation-quality-discarding-most/)
- [↑ back to papers of 2007](https://md.research.elamur.ai/free/en/papers/2007/)

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