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# An Empirical Comparison of Parsing Methods for Stanford Dependencies

**Authors:** [Lingpeng Kong](https://md.mova.elamur.ai/free/en/docs/papers/authors/lingpeng-kong/), [Noah A. Smith](https://md.mova.elamur.ai/free/en/docs/papers/authors/noah-a-smith/) · **Year:** 2014 · **Venue:** arXiv:1404.4314 **Link:** [arxiv.org/abs/1404.4314](https://arxiv.org/abs/1404.4314) · **Licence:** arXiv licence — summary only

[**13** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/enud-2014-kong-empirical-comparison-parsing-methods-stanford/) of the 140673 papers whose bibliographies we readarXiv:1404.4314[licence](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

- [Publisher's page](https://arxiv.org/abs/1404.4314)
- [PDF](https://arxiv.org/pdf/1404.4314)
- [arXiv 1404.4314](https://arxiv.org/abs/1404.4314)

**Authors (2)**

1. Lingpeng Kong
2. Noah A. Smith

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

Stanford dependencies are usually obtained by parsing to constituents and then applying head rules (c-parsing), which is slow. The authors re-examine whether direct dependency parsing (d-parsing) can now match it. On PTB section 22 and 23 with BASIC dependencies, the Charniak-Johnson c-parser gives UAS 93.91 and 94.38, while the best d-parser (Full TurboParser) gives 92.29 and 92.20 at about twice the speed of the c-parser, and a basic first-order TurboParser gives 90.25 at about twenty times the speed of the Stanford English PCFG. The gap for CC-processed dependencies falls to about 2.6 F1 points. Three further gains are reported: part-of-speech tags from the Berkeley parser, which tags as part of phrase-structure parsing, narrow the gap (about one point for the d-parsers), Brown-cluster features add about 0.7 UAS to the best d-parser, and stacking on Yamada-Matsumoto dependencies adds about 1.5 LAS points over MSTParser alone.

## Ideas

- I1. `en/src/ptag.rs` and `en/src/parse.rs`: measure the LAS loss of the Mova parser that comes from tagger errors, by parsing EWT test with gold UPOS and with predicted UPOS, as the paper does for POS tags; this is the first number to get before any parser change.
- I2. `en/src/parse.rs` features: Brown-style clusters learned from our own open corpus in `$MOVA_DATA` (not from third-party text), used in place of word forms for rare words; measure LAS on EWT test against the current 80.24 LAS, run twice.
- I3. A stacked second pass: a second `en` parser trained on another representation (for example the constituency-converted trees from `en/src/convert.rs`) whose predicted arcs become features for the first; measure LAS on EWT test and the time per sentence, as the paper does.
- I4. `en beam-curve` in the paper's speed-accuracy layout: the arc-factored point against the beam-8 point and the `gparse` point, measured on EWT test in tokens per second on the same machine.

## For Mova

Touches `languages/en/20-syntax-overview`, `10-word-classes` (tagger input) and `13-verbs`, and the `en` crate (`ptag.rs`, `parse.rs`, `convert.rs`), `en beam-curve` and `gparse` for the GPU comparison. The paper's PTB numbers are research-licensed: use them as a reference only. Measure first the gold-tag and predicted-tag LAS on EWT test, because it decides how much tagging work is worth doing before parser work.

Topics: [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [arXiv 1404.4314](https://arxiv.org/abs/1404.4314)

```
@misc{kong2014empirical,
  title = {{An Empirical Comparison of Parsing Methods for Stanford Dependencies}},
  author = {Kong, Lingpeng and Smith, Noah A.},
  year = {2014},
  eprint = {1404.4314},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/1404.4314}
}
```

- [← previous Part-of-Speech Tagging from 97% to 100%: Is It Time for Some Linguistics?](https://md.mova.elamur.ai/free/en/docs/papers/enud-2011-manning-part-speech-tagging-97-100/)
- [next → Focusing for Pronoun Resolution in English Discourse: An Implementation](https://md.mova.elamur.ai/free/en/docs/papers/ersan-1994-focusing-pronoun-resolution-english-discourse/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

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

1. J. Björne, J. Heimonen, F. Ginera, A. Airola, T. Pahkkala, and T. Salakoski. Extracting complex biological events with rich graph-based feature sets. In Proc. of BioNLP the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task, 2009. [find](https://scholar.google.com/scholar?q=J%2E+Bjo%CC%88rne%2C+J%2E+Heimonen%2C+F%2E+Ginera%2C+A%2E+Airola%2C+T%2E+Pahkkala%2C+and+T%2E+Salakoski%2E+Extracting+complex+biological+events+with+rich+graph%2Dbased+feature+sets%2E+In+Proc%2E+of+BioNLP+the+Workshop+on+Current+Trends)
2. B. Bohnet. Very high accuracy and fast dependency parsing is not a contradiction. In Proc. of COLING, 2010. [find](https://scholar.google.com/scholar?q=B%2E+Bohnet%2E+Very+high+accuracy+and+fast+dependency+parsing+is+not+a+contradiction%2E+In+Proc%2E+of+COLING%2C+2010%2E)
3. P. Brown, P. Desouza, R. Mercer, V. Pietra, and J. Lai. Class-based n-gram models of natural language. Computational linguistics, 18(4):467–479, 1992. [find](https://scholar.google.com/scholar?q=P%2E+Brown%2C+P%2E+Desouza%2C+R%2E+Mercer%2C+V%2E+Pietra%2C+and+J%2E+Lai%2E+Class%2Dbased+n%2Dgram+models+of+natural+language%2E+Computational+linguistics%2C+18%284%29%3A467%E2%80%93479%2C+1992%2E)
4. D. Cer, M. C. de Marneffe, D. Jurafsky, and C. D Manning. Parsing to Stanford dependencies: Trade-offs between speed and accuracy. In Proc. of LREC, 2010. [find](https://scholar.google.com/scholar?q=D%2E+Cer%2C+M%2E+C%2E+de+Marneffe%2C+D%2E+Jurafsky%2C+and+C%2E+D+Manning%2E+Parsing+to+Stanford+dependencies%3A+Trade%2Doffs+between+speed+and+accuracy%2E+In+Proc%2E+of+LREC%2C+2010%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/bank-2010-cer-parsing-stanford-dependencies-trade-offs/)
5. Ö. Çetinoğlu, J. Foster, J. Nivre, D. Hogan, A. Cahill, and J. Genabith. LFG without C-structures. In Proc. of TLT, 2010. [find](https://scholar.google.com/scholar?q=O%CC%88%2E+C%CC%A7etinog%CC%86lu%2C+J%2E+Foster%2C+J%2E+Nivre%2C+D%2E+Hogan%2C+A%2E+Cahill%2C+and+J%2E+Genabith%2E+LFG+without+C%2Dstructures%2E+In+Proc%2E+of+TLT%2C+2010%2E)
6. C. Chang and C. Lin. Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3):27, 2011. [find](https://scholar.google.com/scholar?q=C%2E+Chang+and+C%2E+Lin%2E+Libsvm%3A+a+library+for+support+vector+machines%2E+ACM+Transactions+on+Intelligent+Systems+and+Technology%2C+2%283%29%3A27%2C+2011%2E)
7. E. Charniak and M. Johnson. Coarse-to-fine n-best parsing and maxent discriminative reranking. In Proc. of ACL, 2005. [find](https://scholar.google.com/scholar?q=E%2E+Charniak+and+M%2E+Johnson%2E+Coarse%2Dto%2Dfine+n%2Dbest+parsing+and+maxent+discriminative+reranking%2E+In+Proc%2E+of+ACL%2C+2005%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/narrative-2005-charniak-coarse-fine-n-best-parsing/)
8. PTB §22 UAS LAS 90.06 87.17 93.11 90.16 92.16 89.50 92.82 90.14 92.29 89.64 92.96 90.31 PTB §23 UAS LAS 90.09 87.48 92.80 90.10 91.95 89.40 92.50 89.95 92.20 89.67 92.75 90.20 Speed (tokens/s.) 123.63 66.57 413.67 243.83 209.98 179.26 E. Charniak, D. Blaheta, N. Ge, K. Hall, J. Hale, and M. Johnson. Bllip 1987-89 wsj corpus release 1. [find](https://scholar.google.com/scholar?q=PTB+%C2%A722+UAS+LAS+90%2E06+87%2E17+93%2E11+90%2E16+92%2E16+89%2E50+92%2E82+90%2E14+92%2E29+89%2E64+92%2E96+90%2E31+PTB+%C2%A723+UAS+LAS+90%2E09+87%2E48+92%2E80+90%2E10+91%2E95+89%2E40+92%2E50+89%2E95+92%2E20+89%2E67+92%2E75+90%2E20+Speed+%28tokens%2Fs%2E%29+123%2E63)
9. Linguistic Data Consortium, Philadelphia, 2000. [find](https://scholar.google.com/scholar?q=Linguistic+Data+Consortium%2C+Philadelphia%2C+2000%2E)
10. W. Che, V. I. Spitkovsky, and T. Liu. A comparison of Chinese parsers for Stanford dependencies. In Proc. of ACL, 2012. [find](https://scholar.google.com/scholar?q=W%2E+Che%2C+V%2E+I%2E+Spitkovsky%2C+and+T%2E+Liu%2E+A+comparison+of+Chinese+parsers+for+Stanford+dependencies%2E+In+Proc%2E+of+ACL%2C+2012%2E)
11. J. Choi and A. McCallum. Transition-based dependency parsing with selectional branching. In Proc. of ACL, 2013. [find](https://scholar.google.com/scholar?q=J%2E+Choi+and+A%2E+McCallum%2E+Transition%2Dbased+dependency+parsing+with+selectional+branching%2E+In+Proc%2E+of+ACL%2C+2013%2E)
12. R. Collobert and J. Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proc. of ICML, 2008. [find](https://scholar.google.com/scholar?q=R%2E+Collobert+and+J%2E+Weston%2E+A+unified+architecture+for+natural+language+processing%3A+Deep+neural+networks+with+multitask+learning%2E+In+Proc%2E+of+ICML%2C+2008%2E)
13. M. C. de Marneffe and C. D Manning. The Stanford typed dependencies representation. In Proc. of COLING workshop on Cross-Framework and Cross-Domain Parser Evaluation, 2008. [find](https://scholar.google.com/scholar?q=M%2E+C%2E+de+Marneffe+and+C%2E+D+Manning%2E+The+Stanford+typed+dependencies+representation%2E+In+Proc%2E+of+COLING+workshop+on+Cross%2DFramework+and+Cross%2DDomain+Parser+Evaluation%2C+2008%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/marneffe-2008-stanford-typed-dependencies-representation/)
14. M. C. de Marneffe, B. MacCartney, and C. D. Manning. Generating typed dependency parses from phrase structure parses. In Proc. of LREC, 2006. [find](https://scholar.google.com/scholar?q=M%2E+C%2E+de+Marneffe%2C+B%2E+MacCartney%2C+and+C%2E+D%2E+Manning%2E+Generating+typed+dependency+parses+from+phrase+structure+parses%2E+In+Proc%2E+of+LREC%2C+2006%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/map-2006-marneffe-generating-typed-dependency-parses-phrase/)
15. J. Elming, A. Johannsen, S. Klerke, E. Lapponi, H. Martinez, and A. Søgaard. Down-stream effects of tree-to-dependency conversions. In Proc. of NAACL-HLT, 2013. [find](https://scholar.google.com/scholar?q=J%2E+Elming%2C+A%2E+Johannsen%2C+S%2E+Klerke%2C+E%2E+Lapponi%2C+H%2E+Martinez%2C+and+A%2E+S%C3%B8gaard%2E+Down%2Dstream+effects+of+tree%2Dto%2Ddependency+conversions%2E+In+Proc%2E+of+NAACL%2DHLT%2C+2013%2E)
16. R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin. Liblinear: A library for large linear classification. The Journal of Machine Learning Research, 9:1871–1874, 2008. [find](https://scholar.google.com/scholar?q=R%2E+Fan%2C+K%2E+Chang%2C+C%2E+Hsieh%2C+X%2E+Wang%2C+and+C%2E+Lin%2E+Liblinear%3A+A+library+for+large+linear+classification%2E+The+Journal+of+Machine+Learning+Research%2C+9%3A1871%E2%80%931874%2C+2008%2E)
17. Y. Goldberg and M. Elhadad. An efficient algorithm for easy-first non-directional dependency parsing. In Proc. of ACL, 2010. [find](https://scholar.google.com/scholar?q=Y%2E+Goldberg+and+M%2E+Elhadad%2E+An+efficient+algorithm+for+easy%2Dfirst+non%2Ddirectional+dependency+parsing%2E+In+Proc%2E+of+ACL%2C+2010%2E)
18. D. Graff. North american news text corpus, 1995. [find](https://scholar.google.com/scholar?q=D%2E+Graff%2E+North+american+news+text+corpus%2C+1995%2E)
19. J. Hatori, T. Matsuzaki, Y. Miyao, and J. Tsujii. Incremental joint pos tagging and dependency parsing in chinese. In Proc. of IJCNLP, 2011. [find](https://scholar.google.com/scholar?q=J%2E+Hatori%2C+T%2E+Matsuzaki%2C+Y%2E+Miyao%2C+and+J%2E+Tsujii%2E+Incremental+joint+pos+tagging+and+dependency+parsing+in+chinese%2E+In+Proc%2E+of+IJCNLP%2C+2011%2E)
20. L. Huang and K. Sagae. Dynamic programming for linear-time incremental parsing. In Proc. of ACL, 2010. [find](https://scholar.google.com/scholar?q=L%2E+Huang+and+K%2E+Sagae%2E+Dynamic+programming+for+linear%2Dtime+incremental+parsing%2E+In+Proc%2E+of+ACL%2C+2010%2E)
21. L. Huang, S. Fayong, and Y. Guo. Structured perceptron with inexact search. In Proc. of NAACL-HLT, 2012. [find](https://scholar.google.com/scholar?q=L%2E+Huang%2C+S%2E+Fayong%2C+and+Y%2E+Guo%2E+Structured+perceptron+with+inexact+search%2E+In+Proc%2E+of+NAACL%2DHLT%2C+2012%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/huang-2012-structured-perceptron-inexact-search/)
22. D. Klein and C. D. Manning. Accurate unlexicalized parsing. In Proc. of ACL, 2003. [find](https://scholar.google.com/scholar?q=D%2E+Klein+and+C%2E+D%2E+Manning%2E+Accurate+unlexicalized+parsing%2E+In+Proc%2E+of+ACL%2C+2003%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2003-klein-accurate-unlexicalized-parsing/)
23. T. Koo and M. Collins. Efficient third-order dependency parsers. In Proc. of ACL, 2010. [find](https://scholar.google.com/scholar?q=T%2E+Koo+and+M%2E+Collins%2E+Efficient+third%2Dorder+dependency+parsers%2E+In+Proc%2E+of+ACL%2C+2010%2E)
24. T. Koo, X. Carreras, and M. Collins. Simple semi-supervised dependency parsing. In Proc. of ACL, 2008. [find](https://scholar.google.com/scholar?q=T%2E+Koo%2C+X%2E+Carreras%2C+and+M%2E+Collins%2E+Simple+semi%2Dsupervised+dependency+parsing%2E+In+Proc%2E+of+ACL%2C+2008%2E)
25. D. Lewis, Y. Yang, T. Rose, and F. Li. Rcv1: A new benchmark collection for text categorization research. The Journal of Machine Learning Research, 5:361–397, 2004. [find](https://scholar.google.com/scholar?q=D%2E+Lewis%2C+Y%2E+Yang%2C+T%2E+Rose%2C+and+F%2E+Li%2E+Rcv1%3A+A+new+benchmark+collection+for+text+categorization+research%2E+The+Journal+of+Machine+Learning+Research%2C+5%3A361%E2%80%93397%2C+2004%2E)
26. Z. Li, M. Zhang, W. Che, T. Liu, W. Chen, and H. Li. Joint models for chinese pos tagging and dependency parsing. In Proc. of EMNLP, 2011. [find](https://scholar.google.com/scholar?q=Z%2E+Li%2C+M%2E+Zhang%2C+W%2E+Che%2C+T%2E+Liu%2C+W%2E+Chen%2C+and+H%2E+Li%2E+Joint+models+for+chinese+pos+tagging+and+dependency+parsing%2E+In+Proc%2E+of+EMNLP%2C+2011%2E)
27. A. F. T Martins, D. Das, N. A. Smith, and E. P. Xing. Stacking dependency parsers. In Proc. of EMNLP, 2008. [find](https://scholar.google.com/scholar?q=A%2E+F%2E+T+Martins%2C+D%2E+Das%2C+N%2E+A%2E+Smith%2C+and+E%2E+P%2E+Xing%2E+Stacking+dependency+parsers%2E+In+Proc%2E+of+EMNLP%2C+2008%2E)
28. A. F. T. Martins, N. A. Smith, E. P. Xing, M. A. T. Figueiredo, and P. M. Q. Aguiar. Turbo parsers: Dependency parsing by approximate variational inference. In Proc. of EMNLP, 2010. [find](https://scholar.google.com/scholar?q=A%2E+F%2E+T%2E+Martins%2C+N%2E+A%2E+Smith%2C+E%2E+P%2E+Xing%2C+M%2E+A%2E+T%2E+Figueiredo%2C+and+P%2E+M%2E+Q%2E+Aguiar%2E+Turbo+parsers%3A+Dependency+parsing+by+approximate+variational+inference%2E+In+Proc%2E+of+EMNLP%2C+2010%2E)
29. A. F. T. Martins, N. A. Smith, P. M. Q. Aguiar, and M. A. T. Figueiredo. Dual decomposition with many overlapping components. In Proc. of EMNLP, 2011. [find](https://scholar.google.com/scholar?q=A%2E+F%2E+T%2E+Martins%2C+N%2E+A%2E+Smith%2C+P%2E+M%2E+Q%2E+Aguiar%2C+and+M%2E+A%2E+T%2E+Figueiredo%2E+Dual+decomposition+with+many+overlapping+components%2E+In+Proc%2E+of+EMNLP%2C+2011%2E)
30. A. F. T. Martins, M. Almeida, and N. A. Smith. Turning on the turbo: Fast third-order non-projective turbo parsers. In Proc. of ACL, 2013. [find](https://scholar.google.com/scholar?q=A%2E+F%2E+T%2E+Martins%2C+M%2E+Almeida%2C+and+N%2E+A%2E+Smith%2E+Turning+on+the+turbo%3A+Fast+third%2Dorder+non%2Dprojective+turbo+parsers%2E+In+Proc%2E+of+ACL%2C+2013%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2013-martins-turning-turbo-fast-third-order/)
31. D. McClosky, E. Charniak, and M. Johnson. Effective self-training for parsing. In Proc. of NAACL-HLT, 2006. [find](https://scholar.google.com/scholar?q=D%2E+McClosky%2C+E%2E+Charniak%2C+and+M%2E+Johnson%2E+Effective+self%2Dtraining+for+parsing%2E+In+Proc%2E+of+NAACL%2DHLT%2C+2006%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2006-mcclosky-effective-self-training-parsing/)
32. R. McDonald and F. Pereira. Online learning of approximate dependency parsing algorithms. In Proc. of EACL, 2006. [find](https://scholar.google.com/scholar?q=R%2E+McDonald+and+F%2E+Pereira%2E+Online+learning+of+approximate+dependency+parsing+algorithms%2E+In+Proc%2E+of+EACL%2C+2006%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/continual-2006-mcdonald-online-learning-approximate-dependency-parsing/)
33. R. McDonald, F. Pereira, K. Ribarov, and J. Hajič. Non-projective dependency parsing using spanning tree algorithms. In Proc. of HLT-EMNLP, 2005. [find](https://scholar.google.com/scholar?q=R%2E+McDonald%2C+F%2E+Pereira%2C+K%2E+Ribarov%2C+and+J%2E+Hajic%CC%8C%2E+Non%2Dprojective+dependency+parsing+using+spanning+tree+algorithms%2E+In+Proc%2E+of+HLT%2DEMNLP%2C+2005%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/narrative-2005-mcdonald-non-projective-dependency-parsing-spanning/)
34. J. Nivre. Incrementality in deterministic dependency parsing. In Proc. of ACL Workshop on Incremental Parsing: Bringing Engineering and Cognition Together, 2004. [find](https://scholar.google.com/scholar?q=J%2E+Nivre%2E+Incrementality+in+deterministic+dependency+parsing%2E+In+Proc%2E+of+ACL+Workshop+on+Incremental+Parsing%3A+Bringing+Engineering+and+Cognition+Together%2C+2004%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/nivre-2004-incrementality-deterministic-dependency-parsing/)
35. J. Nivre. Non-projective dependency parsing in expected linear time. In Proc. of ACL-IJCNLP, 2009. [find](https://scholar.google.com/scholar?q=J%2E+Nivre%2E+Non%2Dprojective+dependency+parsing+in+expected+linear+time%2E+In+Proc%2E+of+ACL%2DIJCNLP%2C+2009%2E)
36. J. Nivre and R. McDonald. Integrating graph-based and transition-based dependency parsers. In Proc. of ACL-HLT, 2008. [find](https://scholar.google.com/scholar?q=J%2E+Nivre+and+R%2E+McDonald%2E+Integrating+graph%2Dbased+and+transition%2Dbased+dependency+parsers%2E+In+Proc%2E+of+ACL%2DHLT%2C+2008%2E)
37. J. Nivre, J. Hall, and J. Nilsson. Maltparser: A data-driven parser-generator for dependency parsing. In Proc. of LREC, 2006. [find](https://scholar.google.com/scholar?q=J%2E+Nivre%2C+J%2E+Hall%2C+and+J%2E+Nilsson%2E+Maltparser%3A+A+data%2Ddriven+parser%2Dgenerator+for+dependency+parsing%2E+In+Proc%2E+of+LREC%2C+2006%2E)
38. J. Nivre, M. Kuhlmann, and J. Hall. An improved oracle for dependency parsing with online reordering. In Proc. of IWPT, 2009. [find](https://scholar.google.com/scholar?q=J%2E+Nivre%2C+M%2E+Kuhlmann%2C+and+J%2E+Hall%2E+An+improved+oracle+for+dependency+parsing+with+online+reordering%2E+In+Proc%2E+of+IWPT%2C+2009%2E)
39. S. Petrov, L. Barrett, R. Thibaux, and D. Klein. Learning accurate, compact, and interpretable tree annotation. In Proc. of COLING-ACL, 2006. [find](https://scholar.google.com/scholar?q=S%2E+Petrov%2C+L%2E+Barrett%2C+R%2E+Thibaux%2C+and+D%2E+Klein%2E+Learning+accurate%2C+compact%2C+and+interpretable+tree+annotation%2E+In+Proc%2E+of+COLING%2DACL%2C+2006%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2006-petrov-learning-accurate-compact-interpretable-tree/)
40. A. M. Rush and S. Petrov. Vine pruning for efficient multi-pass dependency parsing. In Proc. of NAACL, 2012. [find](https://scholar.google.com/scholar?q=A%2E+M%2E+Rush+and+S%2E+Petrov%2E+Vine+pruning+for+efficient+multi%2Dpass+dependency+parsing%2E+In+Proc%2E+of+NAACL%2C+2012%2E)
41. R. Socher, J. Bauer, C. D. Manning, and A. Ng. Parsing with compositional vector grammars. In Proc. of ACL, 2013. [find](https://scholar.google.com/scholar?q=R%2E+Socher%2C+J%2E+Bauer%2C+C%2E+D%2E+Manning%2C+and+A%2E+Ng%2E+Parsing+with+compositional+vector+grammars%2E+In+Proc%2E+of+ACL%2C+2013%2E)
42. D. Stein, S. Peitz, D. Vilar, and H. Ney. A cocktail of deep syntactic features for hierarchical machine translation. In Proc. of AMTA, 2010. [find](https://scholar.google.com/scholar?q=D%2E+Stein%2C+S%2E+Peitz%2C+D%2E+Vilar%2C+and+H%2E+Ney%2E+A+cocktail+of+deep+syntactic+features+for+hierarchical+machine+translation%2E+In+Proc%2E+of+AMTA%2C+2010%2E)
43. J. Tetreault, J. Foster, and M. Chodorow. Using parse features for preposition selection and error detection. In Proc. of ACL, 2010. [find](https://scholar.google.com/scholar?q=J%2E+Tetreault%2C+J%2E+Foster%2C+and+M%2E+Chodorow%2E+Using+parse+features+for+preposition+selection+and+error+detection%2E+In+Proc%2E+of+ACL%2C+2010%2E)
44. K. Toutanova, D. Klein, C. D. Manning, and Y. Singer. Feature-rich part-of-speech tagging with a cyclic dependency network. In Proc. of COLING-ACL, 2003. [find](https://scholar.google.com/scholar?q=K%2E+Toutanova%2C+D%2E+Klein%2C+C%2E+D%2E+Manning%2C+and+Y%2E+Singer%2E+Feature%2Drich+part%2Dof%2Dspeech+tagging+with+a+cyclic+dependency+network%2E+In+Proc%2E+of+COLING%2DACL%2C+2003%2E)
45. J. Turian, L. Ratinov, and Y. Bengio. Word representations: a simple and general method for semi-supervised learning. In Proc. of ACL, 2010. [find](https://scholar.google.com/scholar?q=J%2E+Turian%2C+L%2E+Ratinov%2C+and+Y%2E+Bengio%2E+Word+representations%3A+a+simple+and+general+method+for+semi%2Dsupervised+learning%2E+In+Proc%2E+of+ACL%2C+2010%2E)
46. H. Yamada and Y. Matsumoto. Statistical dependency analysis with support vector machines. In Proc. of IWPT, 2003. [find](https://scholar.google.com/scholar?q=H%2E+Yamada+and+Y%2E+Matsumoto%2E+Statistical+dependency+analysis+with+support+vector+machines%2E+In+Proc%2E+of+IWPT%2C+2003%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/parsers-2003-yamada-statistical-dependency-analysis-support-vector/)
47. H. Zhang, L. Huang, K. Zhao, and R. McDonald. Online learning for inexact hypergraph search. In Proc. of EMNLP, 2013. [find](https://scholar.google.com/scholar?q=H%2E+Zhang%2C+L%2E+Huang%2C+K%2E+Zhao%2C+and+R%2E+McDonald%2E+Online+learning+for+inexact+hypergraph+search%2E+In+Proc%2E+of+EMNLP%2C+2013%2E)