---
title: "Multi-Source Transfer of Delexicalized — McDonald 2011"
description: "The paper tackles dependency parsing for languages that have no annotated treebank."
authors: ["Ryan McDonald", "Slav Petrov", "Keith Hall"]
year: 2011
venue: "Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D11-1006/"
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/mcdonald-2011-multi-source-transfer-delexicalized-dependency/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/mcdonald-2011-multi-source-transfer-delexicalized-dependency/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2011](https://md.research.elamur.ai/free/en/papers/2011/) / Multi-Source Transfer of Delexicalized Dependency Parsers

# Multi-Source Transfer of Delexicalized Dependency Parsers

**Authors:** [Ryan McDonald](https://md.research.elamur.ai/free/en/authors/ryan-mcdonald/), [Slav Petrov](https://md.research.elamur.ai/free/en/authors/slav-petrov/), [Keith Hall](https://md.research.elamur.ai/free/en/authors/keith-hall/) · **Year:** 2011 · **Venue:** Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing · **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/D11-1006/) · [PDF](https://aclanthology.org/D11-1006.pdf) · [OpenAlex](https://openalex.org/W2152691628)

**Citations:** 300 citations (openalex-snapshot · 2026-10-14) · [190](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper tackles dependency parsing for languages that have no annotated treebank. The authors' starting point is that unsupervised grammar induction still trails supervised parsing by a wide margin. Their parser is delexicalized: it is trained on English CoNLL treebank data using only part-of-speech features, with every tagset mapped to the shared universal tagset of Petrov et al. (2011). Because the features are language-neutral, this parser can be run on another language's tag sequences without changes, and that alone beats the unsupervised DMV baseline by a clear margin. In a second step, the transferred model seeds a perceptron for the target language. That perceptron is refined with augmented-loss (constraint-driven) training, which rewards parses that agree with English parses carried over through word alignments in Europarl. Training on treebanks from several source languages at once improves results further. Across eight Indo-European languages, the final parsers beat earlier unsupervised and projection-based systems. A practical consequence is that the score of a delexicalized transferred parser becomes the baseline that new grammar induction work should have to beat.

## Key points
- Setup: CoNLL 2006/2007 treebanks for Danish, Dutch, German, Greek, Italian, Portuguese, Spanish and Swedish, with English used only as a source. Alignments come from Europarl v5 aligned with IBM Model 1 and the HMM model. The parser is an arc-eager transition-based parser trained with the averaged perceptron (beam 8). The metric is unlabeled attachment score (UAS), not LAS, and no treebanks were modified.
- On English, removing lexical features costs about 7 points: 89.3 UAS with them, 82.5 without. Part-of-speech tags alone therefore carry most of the information needed for unlabeled attachment.
- Projection through parallel data cuts relative error by about 8% on average compared with direct English transfer, and by up to 18% for some languages. Multi-source training cuts error by about 16% compared with the English direct-transfer baseline.
- On sentences of 10 words or fewer with gold POS tags, average UAS rises from 66.6 (English direct) to 69.4 (English projected), 71.7 (multi-source direct) and 74.1 (multi-source projected). The unsupervised baseline averages 57.4.
- The multi-source gain holds even when the training data is subsampled down to the size of the English set. Early tests on Arabic, Chinese and Japanese suggest direct transfer is still a reasonable baseline across language families.

*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/) · [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/)

## Cited by

Cited by

190 papers of our library cite it (of the 140673 whose bibliographies we read); 23 of them have a page here, 167 not yet.

- [Out-of-Domain Dependency Parsing for Dialects of Arabic: A Case Study](https://md.research.elamur.ai/free/en/papers/aclbulk-2024-mokh-out-domain-dependency-parsing-dialects/)
- [Universal Dependencies for Croatian (that work for Serbian, too)](https://md.research.elamur.ai/free/en/papers/agic-2015-universal-dependencies-croatian-that-work/)
- [Multilingual Projection for Parsing Truly Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/agic-2016-multilingual-projection-parsing-truly-low/)
- [Cross-Lingual Parser Selection for Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/agic-2017-cross-lingual-parser-selection-low/)
- [Many Languages, One Parser](https://md.research.elamur.ai/free/en/papers/ammar-2016-many-languages-one-parser/)
- [Massively Multilingual Word Embeddings](https://md.research.elamur.ai/free/en/papers/ammar-2016-massively-multilingual-word-embeddings/)
- [A Falta de Pan, Buenas Son Tortas: The Efficacy of Predicted UPOS Tags for Low Resource UD Parsing](https://md.research.elamur.ai/free/en/papers/anderson-2021-falta-de-pan-buenas-son/)
- [Parsing in the absence of related languages: Evaluating low-resource dependency parsers on Tagalog](https://md.research.elamur.ai/free/en/papers/aquino-2020-parsing-absence-related-languages-evaluating/)
- [Cross-lingual and Supervised Models for Morphosyntactic Annotation: a Comparison on Romanian](https://md.research.elamur.ai/free/en/papers/aufrant-2016-cross-lingual-supervised-models-morphosyntactic/)
- [Cross-lingual RST Discourse Parsing](https://md.research.elamur.ai/free/en/papers/braud-2017-cross-lingual-rst-discourse-parsing/)
- [Cross-lingual Dependency Parsing Based on Distributed Representations](https://md.research.elamur.ai/free/en/papers/guo-2015-cross-lingual-dependency-parsing-based/)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://md.research.elamur.ai/free/en/papers/hu-2020-xtreme-massively-multilingual-multi-task/)
- [Transferring Coreference Resolvers with Posterior Regularization](https://md.research.elamur.ai/free/en/papers/martins-2015-transferring-coreference-resolvers-posterior-regularization/)
- [Selective Sharing for Multilingual Dependency Parsing](https://md.research.elamur.ai/free/en/papers/naseem-2012-selective-sharing-multilingual-dependency-parsing/)
- [The Persian Dependency Treebank Made Universal](https://md.research.elamur.ai/free/en/papers/safari-2022-persian-dependency-treebank-made-universal/)
- [Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing](https://md.research.elamur.ai/free/en/papers/schuster-2019-cross-lingual-alignment-contextual-word/)
- [Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog](https://md.research.elamur.ai/free/en/papers/schuster-2019-cross-lingual-transfer-learning-multilingual/)
- [Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure](https://md.research.elamur.ai/free/en/papers/tackstrom-2012-cross-lingual-word-clusters-direct/)
- [Cross-Lingual Dependency Parsing with Universal Dependencies and Predicted PoS Labels](https://md.research.elamur.ai/free/en/papers/tiedemann-2015-cross-lingual-dependency-parsing-universal/)
- [Metaphor Detection with Cross-Lingual Model Transfer](https://md.research.elamur.ai/free/en/papers/tsvetkov-2014-metaphor-detection-cross-lingual-model/)
- [The Galactic Dependencies Treebanks: Getting More Data by Synthesizing New Languages](https://md.research.elamur.ai/free/en/papers/ud-2016-wang-galactic-dependencies-treebanks-getting-more/)
- [Universal Dependencies Parsing for Colloquial Singaporean English](https://md.research.elamur.ai/free/en/papers/ud-2017-wang-universal-dependencies-parsing-colloquial-singaporean/)
- [75 Languages, 1 Model: Parsing Universal Dependencies Universally](https://md.research.elamur.ai/free/en/papers/ud-2019-kondratyuk-75-languages-1-model-parsing/)

## Cite

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

```bibtex
@inproceedings{mcdonald2011multi,
  title = {{Multi-Source Transfer of Delexicalized Dependency Parsers}},
  author = {McDonald, Ryan and Petrov, Slav and Hall, Keith},
  booktitle = {Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing},
  year = {2011},
  url = {https://aclanthology.org/D11-1006/}
}
```

- [← previous Multi-metric optimization for coreference: The UniTN / IITP / Essex submission to the 2011 CONLL Shared Task](https://md.research.elamur.ai/free/en/papers/uryupina-2011-multi-metric-optimization-coreference-unitn/)
- [next → Multilingual Annotation and Disambiguation of Discourse Connectives for Machine Translation](https://md.research.elamur.ai/free/en/papers/meyer-2011-multilingual-annotation-disambiguation-discourse-connectives/)
- [↑ back to papers of 2011](https://md.research.elamur.ai/free/en/papers/2011/)

## References

47 references, 19 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. T. Berg-Kirkpatrick and D. Klein. 2010. Phylogenetic grammar induction. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=T%2E+Berg%2DKirkpatrick+and+D%2E+Klein%2E+2010%2E+Phylogenetic+grammar+induction%2E+In+Proc%2E+of+ACL%2E)
2. P. Blunsom and T. Cohn. 2010. Unsupervised induction of tree substitution grammars for dependency parsing. Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=P%2E+Blunsom+and+T%2E+Cohn%2E+2010%2E+Unsupervised+induction+of+tree+substitution+grammars+for+dependency+parsing%2E+Proc%2E+of+EMNLP%2E)
3. P. F. Brown, V. J. Della Pietra, S. A. Della Pietra, and R. L. Mercer. 1993. The mathematics of statistical machine translation: parameter estimation. Computational Linguistics, 19. [find](https://scholar.google.com/scholar?q=P%2E+F%2E+Brown%2C+V%2E+J%2E+Della+Pietra%2C+S%2E+A%2E+Della+Pietra%2C+and+R%2E+L%2E+Mercer%2E+1993%2E+The+mathematics+of+statistical+machine+translation%3A+parameter+estimation%2E+Computational+Linguistics%2C+19%2E)
4. S. Buchholz and E. Marsi. 2006. CoNLL-X shared task on multilingual dependency parsing. In Proc. of CoNLL. [find](https://scholar.google.com/scholar?q=S%2E+Buchholz+and+E%2E+Marsi%2E+2006%2E+CoNLL%2DX+shared+task+on+multilingual+dependency+parsing%2E+In+Proc%2E+of+CoNLL%2E) [our card](https://md.research.elamur.ai/free/en/papers/parsers-2006-buchholz-conll-x-shared-task-multilingual/)
5. D. Burkett, S. Petrov, J. Blitzer, and D. Klein. 2010. Learning better monolingual models with unannotated bilingual text. In Proc. of CoNLL. [find](https://scholar.google.com/scholar?q=D%2E+Burkett%2C+S%2E+Petrov%2C+J%2E+Blitzer%2C+and+D%2E+Klein%2E+2010%2E+Learning+better+monolingual+models+with+unannotated+bilingual+text%2E+In+Proc%2E+of+CoNLL%2E)
6. G. Carroll and E. Charniak. 1992. Two experiments on learning probabilistic dependency grammars from corpora. In Proc. of the Working Notes of the Workshop Statistically-Based NLP Techniques. [find](https://scholar.google.com/scholar?q=G%2E+Carroll+and+E%2E+Charniak%2E+1992%2E+Two+experiments+on+learning+probabilistic+dependency+grammars+from+corpora%2E+In+Proc%2E+of+the+Working+Notes+of+the+Workshop+Statistically%2DBased+NLP+Techniques%2E)
7. M.W. Chang, L. Ratinov, and D. Roth. 2007. Guiding semi-supervision with constraint-driven learning. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=M%2EW%2E+Chang%2C+L%2E+Ratinov%2C+and+D%2E+Roth%2E+2007%2E+Guiding+semi%2Dsupervision+with+constraint%2Ddriven+learning%2E+In+Proc%2E+of+ACL%2E)
8. M. Chang, D. Goldwasser, D. Roth, and V. Srikumar. 2010. Structured output learning with indirect supervision. In Proc. of ICML. [find](https://scholar.google.com/scholar?q=M%2E+Chang%2C+D%2E+Goldwasser%2C+D%2E+Roth%2C+and+V%2E+Srikumar%2E+2010%2E+Structured+output+learning+with+indirect+supervision%2E+In+Proc%2E+of+ICML%2E)
9. E. Charniak. 2000. A maximum-entropy-inspired parser. In Proc. of NAACL. [find](https://scholar.google.com/scholar?q=E%2E+Charniak%2E+2000%2E+A+maximum%2Dentropy%2Dinspired+parser%2E+In+Proc%2E+of+NAACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/charniak-2000-maximum-entropy-inspired-parser/)
10. S. Clark and J. R. Curran. 2004. Parsing the WSJ using CCG and log-linear models. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=S%2E+Clark+and+J%2E+R%2E+Curran%2E+2004%2E+Parsing+the+WSJ+using+CCG+and+log%2Dlinear+models%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/clark-2004-parsing-wsj-ccg-log-linear/)
11. S.B. Cohen and N.A. Smith. 2009. Shared logistic normal distributions for soft parameter tying in unsupervised grammar induction. In Proc. of NAACL. [find](https://scholar.google.com/scholar?q=S%2EB%2E+Cohen+and+N%2EA%2E+Smith%2E+2009%2E+Shared+logistic+normal+distributions+for+soft+parameter+tying+in+unsupervised+grammar+induction%2E+In+Proc%2E+of+NAACL%2E)
12. S.B. Cohen, D. Das, and N.A. Smith. 2011. Unsupervised structure prediction with non-parallel multilingual guidance. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=S%2EB%2E+Cohen%2C+D%2E+Das%2C+and+N%2EA%2E+Smith%2E+2011%2E+Unsupervised+structure+prediction+with+non%2Dparallel+multilingual+guidance%2E+In+Proc%2E+of+EMNLP%2E)
13. M. Collins, J. Hajič, L. Ramshaw, and C. Tillmann. 1999. A statistical parser for Czech. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=M%2E+Collins%2C+J%2E+Hajic%CC%8C%2C+L%2E+Ramshaw%2C+and+C%2E+Tillmann%2E+1999%2E+A+statistical+parser+for+Czech%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/collins-1999-statistical-parser-czech/)
14. M. Collins. 1997. Three generative, lexicalised models for statistical parsing. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=M%2E+Collins%2E+1997%2E+Three+generative%2C+lexicalised+models+for+statistical+parsing%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/collins-1997-three-generative-lexicalised-models-statistical/)
15. M. Collins. 2002. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=M%2E+Collins%2E+2002%2E+Discriminative+training+methods+for+hidden+markov+models%3A+Theory+and+experiments+with+perceptron+algorithms%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/collins-2002-discriminative-training-methods-hidden-markov/)
16. D. Das and S. Petrov. 2011. Unsupervised part-of-speech tagging with bilingual graph-based projections. In Proc. of ACL-HLT. [find](https://scholar.google.com/scholar?q=D%2E+Das+and+S%2E+Petrov%2E+2011%2E+Unsupervised+part%2Dof%2Dspeech+tagging+with+bilingual+graph%2Dbased+projections%2E+In+Proc%2E+of+ACL%2DHLT%2E) [our card](https://md.research.elamur.ai/free/en/papers/das-2011-unsupervised-part-speech-tagging-bilingual/)
17. K. Ganchev, J. Gillenwater, and B. Taskar. 2009. Dependency grammar induction via bitext projection constraints. In Proc. of ACL-IJCNLP. [find](https://scholar.google.com/scholar?q=K%2E+Ganchev%2C+J%2E+Gillenwater%2C+and+B%2E+Taskar%2E+2009%2E+Dependency+grammar+induction+via+bitext+projection+constraints%2E+In+Proc%2E+of+ACL%2DIJCNLP%2E)
18. K. Ganchev, J. Graça, J. Gillenwater, and B. Taskar. 2010. Posterior regularization for structured latent variable models. Journal of Machine Learning Research. [find](https://scholar.google.com/scholar?q=K%2E+Ganchev%2C+J%2E+Grac%CC%A7a%2C+J%2E+Gillenwater%2C+and+B%2E+Taskar%2E+2010%2E+Posterior+regularization+for+structured+latent+variable+models%2E+Journal+of+Machine+Learning+Research%2E)
19. D. Gildea. 2001. Corpus variation and parser performance. In Proc of EMNLP. [find](https://scholar.google.com/scholar?q=D%2E+Gildea%2E+2001%2E+Corpus+variation+and+parser+performance%2E+In+Proc+of+EMNLP%2E) [our card](https://md.research.elamur.ai/free/en/papers/aclbulk-2001-gildea-corpus-variation-parser-performance/)
20. K. Hall, R. McDonald, J. Katz-Brown, and M. Ringgaard. 2011. Training dependency parsers by jointly optimizing multiple objectives. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=K%2E+Hall%2C+R%2E+McDonald%2C+J%2E+Katz%2DBrown%2C+and+M%2E+Ringgaard%2E+2011%2E+Training+dependency+parsers+by+jointly+optimizing+multiple+objectives%2E+In+Proc%2E+of+EMNLP%2E)
21. R. Hwa, P. Resnik, A. Weinberg, C. Cabezas, and O. Kolak. 2005. Bootstrapping parsers via syntactic projection across parallel texts. Natural Language Engineering, 11(03):311–325. [find](https://scholar.google.com/scholar?q=R%2E+Hwa%2C+P%2E+Resnik%2C+A%2E+Weinberg%2C+C%2E+Cabezas%2C+and+O%2E+Kolak%2E+2005%2E+Bootstrapping+parsers+via+syntactic+projection+across+parallel+texts%2E+Natural+Language+Engineering%2C+11%2803%29%3A311%E2%80%93325%2E)
22. D. Klein and C. D. Manning. 2004. Corpus-based induction of syntactic structure: models of dependency and constituency. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=D%2E+Klein+and+C%2E+D%2E+Manning%2E+2004%2E+Corpus%2Dbased+induction+of+syntactic+structure%3A+models+of+dependency+and+constituency%2E+In+Proc%2E+of+ACL%2E)
23. P. Koehn. 2005. Europarl: A parallel corpus for statistical machine translation. In MT Summit. [find](https://scholar.google.com/scholar?q=P%2E+Koehn%2E+2005%2E+Europarl%3A+A+parallel+corpus+for+statistical+machine+translation%2E+In+MT+Summit%2E)
24. M. P. Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993. Building a large annotated corpus of English: the Penn treebank. Computational Linguistics, 19. [find](https://scholar.google.com/scholar?q=M%2E+P%2E+Marcus%2C+Mary+Ann+Marcinkiewicz%2C+and+Beatrice+Santorini%2E+1993%2E+Building+a+large+annotated+corpus+of+English%3A+the+Penn+treebank%2E+Computational+Linguistics%2C+19%2E) [our card](https://md.research.elamur.ai/free/en/papers/bank-1993-marcus-building-large-annotated-corpus-english/)
25. D. McClosky, E. Charniak, and M. Johnson. 2006. Reranking and self-training for parser adaptation. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=D%2E+McClosky%2C+E%2E+Charniak%2C+and+M%2E+Johnson%2E+2006%2E+Reranking+and+self%2Dtraining+for+parser+adaptation%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/aclbulk-2006-mcclosky-reranking-self-training-parser-adaptation/)
26. R. McDonald, K. Crammer, and F. Pereira. 2005. Online large-margin training of dependency parsers. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=R%2E+McDonald%2C+K%2E+Crammer%2C+and+F%2E+Pereira%2E+2005%2E+Online+large%2Dmargin+training+of+dependency+parsers%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/narrative-2005-mcdonald-online-large-margin-training-dependency/)
27. T. Naseem, H. Chen, R. Barzilay, and M. Johnson. 2010. Using universal linguistic knowledge to guide grammar induction. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=T%2E+Naseem%2C+H%2E+Chen%2C+R%2E+Barzilay%2C+and+M%2E+Johnson%2E+2010%2E+Using+universal+linguistic+knowledge+to+guide+grammar+induction%2E+In+Proc%2E+of+EMNLP%2E)
28. J. Nivre and J. Nilsson. 2005. Pseudo-projective dependency parsing. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=J%2E+Nivre+and+J%2E+Nilsson%2E+2005%2E+Pseudo%2Dprojective+dependency+parsing%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/nivre-2005-pseudo-projective-dependency-parsing/)
29. J. Nivre, J. Hall, and J. Nilsson. 2006. Maltparser: A data-driven parser-generator for dependency parsing. In Proc. of LREC. [find](https://scholar.google.com/scholar?q=J%2E+Nivre%2C+J%2E+Hall%2C+and+J%2E+Nilsson%2E+2006%2E+Maltparser%3A+A+data%2Ddriven+parser%2Dgenerator+for+dependency+parsing%2E+In+Proc%2E+of+LREC%2E) [our card](https://md.research.elamur.ai/free/en/papers/nivre-2006-maltparser-data-driven-parser-generator/)
30. J. Nivre, J. Hall, S. Kübler, R. McDonald, J. Nilsson, S. Riedel, and D. Yuret. 2007. [find](https://scholar.google.com/scholar?q=J%2E+Nivre%2C+J%2E+Hall%2C+S%2E+Ku%CC%88bler%2C+R%2E+McDonald%2C+J%2E+Nilsson%2C+S%2E+Riedel%2C+and+D%2E+Yuret%2E+2007%2E)
31. The CoNLL 2007 shared task on dependency parsing. In Proc. of EMNLP-CoNLL. [find](https://scholar.google.com/scholar?q=The+CoNLL+2007+shared+task+on+dependency+parsing%2E+In+Proc%2E+of+EMNLP%2DCoNLL%2E) [our card](https://md.research.elamur.ai/free/en/papers/nivre-2007-conll-2007-shared-task-dependency/)
32. J. Nivre. 2008. Algorithms for deterministic incremental dependency parsing. Computational Linguistics, 34(4):513–553. [find](https://scholar.google.com/scholar?q=J%2E+Nivre%2E+2008%2E+Algorithms+for+deterministic+incremental+dependency+parsing%2E+Computational+Linguistics%2C+34%284%29%3A513%E2%80%93553%2E) [our card](https://md.research.elamur.ai/free/en/papers/narrative-2008-nivre-algorithms-deterministic-incremental-dependency-parsing/)
33. S. Petrov, L. Barrett, R. Thibaux, and D. Klein. 2006. Learning accurate, compact, and interpretable tree annotation. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=S%2E+Petrov%2C+L%2E+Barrett%2C+R%2E+Thibaux%2C+and+D%2E+Klein%2E+2006%2E+Learning+accurate%2C+compact%2C+and+interpretable+tree+annotation%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/aclbulk-2006-petrov-learning-accurate-compact-interpretable-tree/)
34. S. Petrov, P. Chang, M. Ringgaard, and H. Alshawi. 2010. Uptraining for accurate deterministic question parsing. In EMNLP ’10. [find](https://scholar.google.com/scholar?q=S%2E+Petrov%2C+P%2E+Chang%2C+M%2E+Ringgaard%2C+and+H%2E+Alshawi%2E+2010%2E+Uptraining+for+accurate+deterministic+question+parsing%2E+In+EMNLP+%E2%80%9910%2E)
35. S. Petrov, D. Das, and R. McDonald. 2011. A universal part-of-speech tagset. In ArXiv:1104.2086. [find](https://scholar.google.com/scholar?q=S%2E+Petrov%2C+D%2E+Das%2C+and+R%2E+McDonald%2E+2011%2E+A+universal+part%2Dof%2Dspeech+tagset%2E+In+ArXiv%3A1104%2E2086%2E)
36. Y. Seginer. 2007. Fast unsupervised incremental parsing. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=Y%2E+Seginer%2E+2007%2E+Fast+unsupervised+incremental+parsing%2E+In+Proc%2E+of+ACL%2E)
37. L. Shen and A.K. Joshi. 2008. Ltag dependency parsing with bidirectional incremental construction. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=L%2E+Shen+and+A%2EK%2E+Joshi%2E+2008%2E+Ltag+dependency+parsing+with+bidirectional+incremental+construction%2E+In+Proc%2E+of+EMNLP%2E)
38. N.A. Smith and J. Eisner. 2005. Contrastive estimation: Training log-linear models on unlabeled data. In Proc. of ACL. [find](https://scholar.google.com/scholar?q=N%2EA%2E+Smith+and+J%2E+Eisner%2E+2005%2E+Contrastive+estimation%3A+Training+log%2Dlinear+models+on+unlabeled+data%2E+In+Proc%2E+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/smith-2005-contrastive-estimation-training-log-linear/)
39. D.A. Smith and J. Eisner. 2009. Parser adaptation and projection with quasi-synchronous grammar features. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=D%2EA%2E+Smith+and+J%2E+Eisner%2E+2009%2E+Parser+adaptation+and+projection+with+quasi%2Dsynchronous+grammar+features%2E+In+Proc%2E+of+EMNLP%2E)
40. D.A. Smith and N.A. Smith. 2004. Bilingual parsing with factored estimation: Using english to parse korean. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=D%2EA%2E+Smith+and+N%2EA%2E+Smith%2E+2004%2E+Bilingual+parsing+with+factored+estimation%3A+Using+english+to+parse+korean%2E+In+Proc%2E+of+EMNLP%2E)
41. B. Snyder, T. Naseem, J. Eisenstein, and R. Barzilay. 2009. Adding more languages improves unsupervised multilingual part-of-speech tagging: A Bayesian non-parametric approach. In Proc. of NAACL. [find](https://scholar.google.com/scholar?q=B%2E+Snyder%2C+T%2E+Naseem%2C+J%2E+Eisenstein%2C+and+R%2E+Barzilay%2E+2009%2E+Adding+more+languages+improves+unsupervised+multilingual+part%2Dof%2Dspeech+tagging%3A+A+Bayesian+non%2Dparametric+approach%2E+In+Proc%2E+of+NAACL%2E)
42. A. Søgaard. 2011. Data point selection for cross-language adaptation of dependency parsers. In Proc. ACL. [find](https://scholar.google.com/scholar?q=A%2E+S%C3%B8gaard%2E+2011%2E+Data+point+selection+for+cross%2Dlanguage+adaptation+of+dependency+parsers%2E+In+Proc%2E+ACL%2E)
43. V.I. Spitkovsky, H. Alshawi, and D. Jurafsky. 2010. From baby steps to leapfrog: How “less is more” in unsupervised dependency parsing. In Proc. of NAACL-HLT. [find](https://scholar.google.com/scholar?q=V%2EI%2E+Spitkovsky%2C+H%2E+Alshawi%2C+and+D%2E+Jurafsky%2E+2010%2E+From+baby+steps+to+leapfrog%3A+How+%E2%80%9Cless+is+more%E2%80%9D+in+unsupervised+dependency+parsing%2E+In+Proc%2E+of+NAACL%2DHLT%2E)
44. S. Vogel, H. Ney, and C. Tillmann. 1996. HMM-based word alignment in statistical translation. In Proc. of COLING. [find](https://scholar.google.com/scholar?q=S%2E+Vogel%2C+H%2E+Ney%2C+and+C%2E+Tillmann%2E+1996%2E+HMM%2Dbased+word+alignment+in+statistical+translation%2E+In+Proc%2E+of+COLING%2E)
45. W. Wang and M. P. Harper. 2004. A statistical constraint dependency grammar (CDG) parser. In Proc. of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together. [find](https://scholar.google.com/scholar?q=W%2E+Wang+and+M%2E+P%2E+Harper%2E+2004%2E+A+statistical+constraint+dependency+grammar+%28CDG%29+parser%2E+In+Proc%2E+of+the+Workshop+on+Incremental+Parsing%3A+Bringing+Engineering+and+Cognition+Together%2E)
46. D. Zeman and P. Resnik. 2008. Cross-language parser adaptation between related languages. In NLP for Less Privileged Languages. [find](https://scholar.google.com/scholar?q=D%2E+Zeman+and+P%2E+Resnik%2E+2008%2E+Cross%2Dlanguage+parser+adaptation+between+related+languages%2E+In+NLP+for+Less+Privileged+Languages%2E) [our card](https://md.research.elamur.ai/free/en/papers/zeman-2008-cross-language-parser-adaptation-between/)
47. Y. Zhang and S. Clark. 2008. A Tale of Two Parsers: Investigating and Combining Graph-based and Transition-based Dependency Parsing. In Proc. of EMNLP. [find](https://scholar.google.com/scholar?q=Y%2E+Zhang+and+S%2E+Clark%2E+2008%2E+A+Tale+of+Two+Parsers%3A+Investigating+and+Combining+Graph%2Dbased+and+Transition%2Dbased+Dependency+Parsing%2E+In+Proc%2E+of+EMNLP%2E) [our card](https://md.research.elamur.ai/free/en/papers/zhang-2008-tale-two-parsers-investigating-combining/)