[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/cl-2008-miyao-feature-forest-models-probabilistic-hpsg/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [English](https://md.mova.elamur.ai/free/en/docs/papers/en/) / Feature Forest Models for Probabilistic HPSG Parsing

# Feature Forest Models for Probabilistic HPSG Parsing

**Authors:** [Yusuke Miyao](https://md.mova.elamur.ai/free/en/docs/papers/authors/yusuke-miyao/) and [Jun'ichi Tsujii](https://md.mova.elamur.ai/free/en/docs/papers/authors/jun-ichi-tsujii/) · **Year:** 2008 · **Venue:** Computational Linguistics, volume 34, number 1 **Link:** [aclanthology.org/J08-1002](https://aclanthology.org/J08-1002/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**200** citations](https://openalex.org/works?filter=cites:W1965893653) openalex · 2026-10-18[**79** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/cl-2008-miyao-feature-forest-models-probabilistic-hpsg/) of the 140673 papers whose bibliographies we read[Computational Linguistics](https://aclanthology.org/volumes/J08-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/J08-1002/)
- [PDF](https://aclanthology.org/J08-1002.pdf)
- [DOI 10.1162/coli.2008.34.1.35](https://doi.org/10.1162/coli.2008.34.1.35)
- [OpenAlex](https://openalex.org/W1965893653)

**Authors (2)**

1. [Yusuke Miyao](https://openalex.org/A5004444958) [ORCID](https://orcid.org/0000-0002-0678-3400) · h 35 · 4576 citations · 263 works
2. [Jun'ichi Tsujii](https://openalex.org/A5112241147) h 62 · 16606 citations · 654 works

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

## What it is about

Probabilistic models for lexicalized grammars are hard because their structures are typed feature structures, not trees or lattices. The paper proposes feature forest models: maximum entropy models over packed forests, estimated by dynamic programming without unpacking the forest and without an independence assumption. It also shows how HPSG syntax and predicate-argument structure are encoded as forests. On Penn Treebank section 23 (sentences under 40 words) the parser covers 99.7% of the sentences, with labelled precision and recall of 87.69% and 87.16% and sentence accuracy of 39.2%. Covered sentences score about 2.5 points higher than the overall F-score, and uncovered ones more than 10 points lower.

## Ideas

- I1. `en/src/parse.rs`: store each token's candidate heads with their feature sums in a packed table, so that a perceptron update touches all candidates at once. Measure LAS and training seconds on EWT dev, checked with `en folds`.
- I2. Lexicon coverage split: as the paper does, split EWT test sentences into those whose words all have the right tags in `en/src/lexgen.rs` and those that do not. Report LAS for each group with `en lexicon` output.
- I3. Feature-group ablation: drop POS features alone and then with a second group, as the paper does, to find which features overlap. Run `en parse-eval` on EWT test.

## For Mova

The code is `en/src/parse.rs` and the dictionary in `en/src/lexgen.rs`; the chapters are [10. Word classes](https://md.mova.elamur.ai/free/en/docs/languages/en/10-word-classes/) and `13-verbs.md`. The paper is HPSG with predicate-argument output, not UD, and its data are PTB-based, outside our licence list. Test on EWT only.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.1162/coli.2008.34.1.35](https://doi.org/10.1162/coli.2008.34.1.35)

```
@article{miyao2008feature,
  title = {{Feature Forest Models for Probabilistic HPSG Parsing}},
  author = {Miyao, Yusuke and Tsujii, Jun'ichi},
  journal = {Computational Linguistics},
  year = {2008},
  doi = {10.1162/coli.2008.34.1.35},
  url = {https://doi.org/10.1162/coli.2008.34.1.35}
}
```

- [← previous Prepositional Phrase Attachment without Oracles](https://md.mova.elamur.ai/free/en/docs/papers/cl-2007-atterer-squibs-prepositional-phrase-attachment-without/)
- [next → Are Ellipses Important for Machine Translation?](https://md.mova.elamur.ai/free/en/docs/papers/cl-2021-khullar-are-ellipses-important-machine-translation/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

83 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Abney, Steven P. 1997. Stochastic attribute-value grammars. Computational Linguistics, 23(4):597–618. [find](https://scholar.google.com/scholar?q=Abney%2C+Steven+P%2E+1997%2E+Stochastic+attribute%2Dvalue+grammars%2E+Computational+Linguistics%2C+23%284%29%3A597%E2%80%93618%2E)
2. Baker, James K. 1979. Trainable grammars for speech recognition. In Jared J. Wolf and Dennis H. Klatt, editors, Speech Communication Papers Presented at the 97th Meeting of the Acoustical Society of America. MIT Press, Cambridge, MA, pages 547–550. [find](https://scholar.google.com/scholar?q=Baker%2C+James+K%2E+1979%2E+Trainable+grammars+for+speech+recognition%2E+In+Jared+J%2E+Wolf+and+Dennis+H%2E+Klatt%2C+editors%2C+Speech+Communication+Papers+Presented+at+the+97th+Meeting+of+the+Acoustical+Society+of+A)
3. Baldridge, Jason and Miles Osborne. 2003. Active learning for HPSG parse selection. In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, pages 17–24, Edmonton, Canada. [find](https://scholar.google.com/scholar?q=Baldridge%2C+Jason+and+Miles+Osborne%2E+2003%2E+Active+learning+for+HPSG+parse+selection%2E+In+Proceedings+of+the+Seventh+Conference+on+Natural+Language+Learning+at+HLT%2DNAACL+2003%2C+pages+17%E2%80%9324%2C+Edmonton%2C+Cana)
4. Berger, Adam L., Stephen A. Della Pietra, and Vincent J. Della Pietra. 1996. A maximum entropy approach to natural language processing. Computational Linguistics, 22(1):39–71. [find](https://scholar.google.com/scholar?q=Berger%2C+Adam+L%2E%2C+Stephen+A%2E+Della+Pietra%2C+and+Vincent+J%2E+Della+Pietra%2E+1996%2E+A+maximum+entropy+approach+to+natural+language+processing%2E+Computational+Linguistics%2C+22%281%29%3A39%E2%80%9371%2E)
5. Burke, Michael, Aoife Cahill, Ruth O’Donovan, Josef van Genabith, and Andy Way. 2004. Treebank-based acquisition of wide-coverage, probabilistic LFG resources: Project overview, results and evaluation. In Proceedings of the IJCNLP-04 Workshop “Beyond Shallow Analyses”, Hainan Island. Available at ww… [find](https://scholar.google.com/scholar?q=Burke%2C+Michael%2C+Aoife+Cahill%2C+Ruth+O%E2%80%99Donovan%2C+Josef+van+Genabith%2C+and+Andy+Way%2E+2004%2E+Treebank%2Dbased+acquisition+of+wide%2Dcoverage%2C+probabilistic+LFG+resources%3A+Project+overview%2C+results+and+evaluation)
6. Carpenter, Bob. 1992. The Logic of Typed Feature Structures. Cambridge University Press, Cambridge, England. [find](https://scholar.google.com/scholar?q=Carpenter%2C+Bob%2E+1992%2E+The+Logic+of+Typed+Feature+Structures%2E+Cambridge+University+Press%2C+Cambridge%2C+England%2E)
7. Carroll, John and Stephan Oepen. 2005. High efficiency realization for a wide-coverage unification grammar. In Proceedings of the 2nd International Joint Conference on Natural Language Processing (IJCNLP-05), pages 165–176, Jeju Island. [find](https://scholar.google.com/scholar?q=Carroll%2C+John+and+Stephan+Oepen%2E+2005%2E+High+efficiency+realization+for+a+wide%2Dcoverage+unification+grammar%2E+In+Proceedings+of+the+2nd+International+Joint+Conference+on+Natural+Language+Processing+%28IJC)
8. Charniak, Eugene. 2000. A maximum-entropy-inspired parser. In Proceedings of the First Conference on North American Chapter of the Association for Computational Linguistics (NAACL 2000), pages 132–139, Seattle, WA. [find](https://scholar.google.com/scholar?q=Charniak%2C+Eugene%2E+2000%2E+A+maximum%2Dentropy%2Dinspired+parser%2E+In+Proceedings+of+the+First+Conference+on+North+American+Chapter+of+the+Association+for+Computational+Linguistics+%28NAACL+2000%29%2C+pages+132%E2%80%93139)
9. Charniak, Eugene and Mark Johnson. 2005. Coarse-to-fine n-best parsing and MaxEnt discriminative reranking. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pages 173–180, Ann Arbor, MI. [find](https://scholar.google.com/scholar?q=Charniak%2C+Eugene+and+Mark+Johnson%2E+2005%2E+Coarse%2Dto%2Dfine+n%2Dbest+parsing+and+MaxEnt+discriminative+reranking%2E+In+Proceedings+of+the+43rd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28) [our card](https://md.mova.elamur.ai/free/en/docs/papers/narrative-2005-charniak-coarse-fine-n-best-parsing/)
10. Chen, Stanley and Ronald Rosenfeld. 1999a. A Gaussian prior for smoothing maximum entropy models. Technical Report CMUCS-99-108, Carnegie Mellon University. [find](https://scholar.google.com/scholar?q=Chen%2C+Stanley+and+Ronald+Rosenfeld%2E+1999a%2E+A+Gaussian+prior+for+smoothing+maximum+entropy+models%2E+Technical+Report+CMUCS%2D99%2D108%2C+Carnegie+Mellon+University%2E)
11. Chen, Stanley F. and Ronald Rosenfeld. 1999b. Efficient sampling and feature selection in whole sentence maximum entropy language models. In Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 549–552, Phoenix, AZ. [find](https://scholar.google.com/scholar?q=Chen%2C+Stanley+F%2E+and+Ronald+Rosenfeld%2E+1999b%2E+Efficient+sampling+and+feature+selection+in+whole+sentence+maximum+entropy+language+models%2E+In+Proceedings+of+the+1999+IEEE+International+Conference+on+Ac)
12. Chiang, David. 2003. Mildly context sensitive grammars for estimating maximum entropy parsing models. In Proceedings of the 8th Conference on Formal Grammar, pages 19–31, Vienna. [find](https://scholar.google.com/scholar?q=Chiang%2C+David%2E+2003%2E+Mildly+context+sensitive+grammars+for+estimating+maximum+entropy+parsing+models%2E+In+Proceedings+of+the+8th+Conference+on+Formal+Grammar%2C+pages+19%E2%80%9331%2C+Vienna%2E)
13. Chun, Hong-Woo. 2007. Mining Literature for Disease-Gene Relations. Ph.D. thesis, University of Tokyo. [find](https://scholar.google.com/scholar?q=Chun%2C+Hong%2DWoo%2E+2007%2E+Mining+Literature+for+Disease%2DGene+Relations%2E+Ph%2ED%2E+thesis%2C+University+of+Tokyo%2E)
14. Clark, Stephen and James R. Curran. 2003. Log-linear models for wide-coverage CCG parsing. In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing (EMNLP 2003), pages 97–104, Sapporo. [find](https://scholar.google.com/scholar?q=Clark%2C+Stephen+and+James+R%2E+Curran%2E+2003%2E+Log%2Dlinear+models+for+wide%2Dcoverage+CCG+parsing%2E+In+Proceedings+of+the+2003+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP+2003%29%2C+pages)
15. Clark, Stephen and James R. Curran. 2004a. The importance of supertagging for wide-coverage CCG parsing. In Proceedings of the 20th International Conference on Computational Linguistics (COLING 2004), pages 282–288, Geneva. [find](https://scholar.google.com/scholar?q=Clark%2C+Stephen+and+James+R%2E+Curran%2E+2004a%2E+The+importance+of+supertagging+for+wide%2Dcoverage+CCG+parsing%2E+In+Proceedings+of+the+20th+International+Conference+on+Computational+Linguistics+%28COLING+2004%29%2C) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2004-clark-importance-supertagging-wide-coverage-ccg/)
16. Clark, Stephen and James R. Curran. 2004b. Parsing the WSJ using CCG and log-linear models. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004), pages 104–111, Barcelona. [find](https://scholar.google.com/scholar?q=Clark%2C+Stephen+and+James+R%2E+Curran%2E+2004b%2E+Parsing+the+WSJ+using+CCG+and+log%2Dlinear+models%2E+In+Proceedings+of+the+42nd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28ACL+2004%29%2C+pages)
17. Clark, Stephen, Julia Hockenmaier, and Mark Steedman. 2002. Building deep dependency structures with a wide-coverage CCG parser. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002), pages 327–334, Philadephia. [find](https://scholar.google.com/scholar?q=Clark%2C+Stephen%2C+Julia+Hockenmaier%2C+and+Mark+Steedman%2E+2002%2E+Building+deep+dependency+structures+with+a+wide%2Dcoverage+CCG+parser%2E+In+Proceedings+of+the+40th+Annual+Meeting+of+the+Association+for+Comput)
18. Cohen, Paul R. 1995. Empirical Methods for Artificial Intelligence. The MIT Press, Cambridge, MA. [find](https://scholar.google.com/scholar?q=Cohen%2C+Paul+R%2E+1995%2E+Empirical+Methods+for+Artificial+Intelligence%2E+The+MIT+Press%2C+Cambridge%2C+MA%2E)
19. Collins, Michael. 1997. Three generative, lexicalised models for statistical parsing. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL’97), pages 16–23, Madrid. [find](https://scholar.google.com/scholar?q=Collins%2C+Michael%2E+1997%2E+Three+generative%2C+lexicalised+models+for+statistical+parsing%2E+In+Proceedings+of+the+35th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28ACL%E2%80%9997%29%2C+pages+16%E2%80%9323%2C+)
20. Collins, Michael. 1999. Head-Driven Statistical Models for Natural Language Parsing. Ph.D. thesis, University of Pennsylvania. [find](https://scholar.google.com/scholar?q=Collins%2C+Michael%2E+1999%2E+Head%2DDriven+Statistical+Models+for+Natural+Language+Parsing%2E+Ph%2ED%2E+thesis%2C+University+of+Pennsylvania%2E)
21. Collins, Michael. 2000. Discriminative reranking for natural language parsing. In Proceedings of the Seventeenth International Conference on Machine Learning, pages 175–182, Palo Alto, CA. [find](https://scholar.google.com/scholar?q=Collins%2C+Michael%2E+2000%2E+Discriminative+reranking+for+natural+language+parsing%2E+In+Proceedings+of+the+Seventeenth+International+Conference+on+Machine+Learning%2C+pages+175%E2%80%93182%2C+Palo+Alto%2C+CA%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/collins-2000-discriminative-reranking-natural-language-parsing/)
22. Collins, Michael. 2003. Head-driven statistical models for natural language parsing. Computational Linguistics, 29(4):589–637. [find](https://scholar.google.com/scholar?q=Collins%2C+Michael%2E+2003%2E+Head%2Ddriven+statistical+models+for+natural+language+parsing%2E+Computational+Linguistics%2C+29%284%29%3A589%E2%80%93637%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/cl-2003-collins-head-driven-statistical-models-natural/)
23. Copestake, Ann, Dan Flickinger, Rob Malouf, Susanne Riehemann, and Ivan Sag. 1995. Translation using minimal recursion semantics. In Proceedings of the Sixth International Conference on Theoretical and Methodological Issues in Machine Translation (TMI95), pages 15–32, Leuven. [find](https://scholar.google.com/scholar?q=Copestake%2C+Ann%2C+Dan+Flickinger%2C+Rob+Malouf%2C+Susanne+Riehemann%2C+and+Ivan+Sag%2E+1995%2E+Translation+using+minimal+recursion+semantics%2E+In+Proceedings+of+the+Sixth+International+Conference+on+Theoretical+an) [our card](https://md.mova.elamur.ai/free/en/docs/papers/copestake-1995-translation-minimal-recursion-semantics/)
24. Copestake, Ann, Dan Flickinger, Ivan A. Sag, and Carl Pollard. 2006. Minimal recursion semantics: An introduction. Research on Language and Computation, 3(4):281–332. [find](https://scholar.google.com/scholar?q=Copestake%2C+Ann%2C+Dan+Flickinger%2C+Ivan+A%2E+Sag%2C+and+Carl+Pollard%2E+2006%2E+Minimal+recursion+semantics%3A+An+introduction%2E+Research+on+Language+and+Computation%2C+3%284%29%3A281%E2%80%93332%2E)
25. Darroch, J. N. and D. Ratcliff. 1972. Generalized iterative scaling for log-linear models. The Annals of Mathematical Statistics, 43(5):1470–1480. [find](https://scholar.google.com/scholar?q=Darroch%2C+J%2E+N%2E+and+D%2E+Ratcliff%2E+1972%2E+Generalized+iterative+scaling+for+log%2Dlinear+models%2E+The+Annals+of+Mathematical+Statistics%2C+43%285%29%3A1470%E2%80%931480%2E)
26. Della Pietra, Stephen, Vincent Della Pietra, and John Lafferty. 1997. Inducing features of random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4):380–393. [find](https://scholar.google.com/scholar?q=Della+Pietra%2C+Stephen%2C+Vincent+Della+Pietra%2C+and+John+Lafferty%2E+1997%2E+Inducing+features+of+random+fields%2E+IEEE+Transactions+on+Pattern+Analysis+and+Machine+Intelligence%2C+19%284%29%3A380%E2%80%93393%2E)
27. Geman, Stuart and Mark Johnson. 2002. Dynamic programming for parsing and estimation of stochastic unification-based grammars. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002), pages 279–286, Philadelphia, PA. [find](https://scholar.google.com/scholar?q=Geman%2C+Stuart+and+Mark+Johnson%2E+2002%2E+Dynamic+programming+for+parsing+and+estimation+of+stochastic+unification%2Dbased+grammars%2E+In+Proceedings+of+the+40th+Annual+Meeting+of+the+Association+for+Computat)
28. Gildea, Daniel. 2001. Corpus variation and parser performance. In Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing (EMNLP 2001), pages 167–202, Pittsburgh, PA. [find](https://scholar.google.com/scholar?q=Gildea%2C+Daniel%2E+2001%2E+Corpus+variation+and+parser+performance%2E+In+Proceedings+of+the+2001+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP+2001%29%2C+pages+167%E2%80%93202%2C+Pittsburgh%2C+PA%2E) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2001-gildea-corpus-variation-parser-performance/)
29. Hockenmaier, Julia. 2003. Parsing with generative models of predicate-argument structure. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics (ACL 2003), pages 359–366, Sapporo. [find](https://scholar.google.com/scholar?q=Hockenmaier%2C+Julia%2E+2003%2E+Parsing+with+generative+models+of+predicate%2Dargument+structure%2E+In+Proceedings+of+the+41st+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28ACL+2003%29%2C+pages+3)
30. Hockenmaier, Julia and Mark Steedman. 2002. Acquiring compact lexicalized grammars from a cleaner treebank. In Proceedings of the Third International Conference on Language Resources and Evaluation (LREC-2002), pages 1974–1981, Las Palmas. [find](https://scholar.google.com/scholar?q=Hockenmaier%2C+Julia+and+Mark+Steedman%2E+2002%2E+Acquiring+compact+lexicalized+grammars+from+a+cleaner+treebank%2E+In+Proceedings+of+the+Third+International+Conference+on+Language+Resources+and+Evaluation+%28L)
31. Johnson, Mark, Stuart Geman, Stephen Canon, Zhiyi Chi, and Stefan Riezler. 1999. Estimators for stochastic “unification-based” grammars. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL’99), pages 535–541, College Park, Maryland. [find](https://scholar.google.com/scholar?q=Johnson%2C+Mark%2C+Stuart+Geman%2C+Stephen+Canon%2C+Zhiyi+Chi%2C+and+Stefan+Riezler%2E+1999%2E+Estimators+for+stochastic+%E2%80%9Cunification%2Dbased%E2%80%9D+grammars%2E+In+Proceedings+of+the+37th+Annual+Meeting+of+the+Association+fo)
32. Johnson, Mark and Stefan Riezler. 2000. Exploiting auxiliary distributions in stochastic unification-based grammars. In Proceedings of the First Conference on North American Chapter of the Association for Computational Linguistics, pages 154–161, Seattle, WA. [find](https://scholar.google.com/scholar?q=Johnson%2C+Mark+and+Stefan+Riezler%2E+2000%2E+Exploiting+auxiliary+distributions+in+stochastic+unification%2Dbased+grammars%2E+In+Proceedings+of+the+First+Conference+on+North+American+Chapter+of+the+Association)
33. Kameya, Yoshitaka and Taisuke Sato. 2000. Efficient EM learning with tabulation for parameterized logic programs. In Proceedings of the 1st International Conference on Computational Logic (CL2000), volume 1861 of Lecture Notes in Artificial Intelligence (LNAI), pages 269–294, Imperial College, Londo… [find](https://scholar.google.com/scholar?q=Kameya%2C+Yoshitaka+and+Taisuke+Sato%2E+2000%2E+Efficient+EM+learning+with+tabulation+for+parameterized+logic+programs%2E+In+Proceedings+of+the+1st+International+Conference+on+Computational+Logic+%28CL2000%29%2C+vo)
34. Kaplan, Ronald M., Stefan Riezler, Tracy H. King, John T. Maxwell, III, Alexander Vasserman, and Richard Crouch. 2004. Speed and accuracy in shallow and deep stochastic parsing. In Proceedings of the Human Language Technology Conference and the North American Chapter of the Association for Computati… [find](https://scholar.google.com/scholar?q=Kaplan%2C+Ronald+M%2E%2C+Stefan+Riezler%2C+Tracy+H%2E+King%2C+John+T%2E+Maxwell%2C+III%2C+Alexander+Vasserman%2C+and+Richard+Crouch%2E+2004%2E+Speed+and+accuracy+in+shallow+and+deep+stochastic+parsing%2E+In+Proceedings+of+the+)
35. Lafferty, John, Andrew McCallum, and Fernando Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the International Conference on Machine Learning 2001, pages 282–289, Williams College, Williamstown, MA. [find](https://scholar.google.com/scholar?q=Lafferty%2C+John%2C+Andrew+McCallum%2C+and+Fernando+Pereira%2E+2001%2E+Conditional+random+fields%3A+Probabilistic+models+for+segmenting+and+labeling+sequence+data%2E+In+Proceedings+of+the+International+Conference+o)
36. Makino, Takaki, Yusuke Miyao, Kentaro Torisawa, and Jun’ichi Tsujii. 2002. Native-code compilation of feature structures. In Stephen Oepen, Dan Flickinger, Jun’ichi Tsujii, and Hans Uszkoreit, editors, Collaborative Language Engineering: A Case Study in Efficient Grammar-based Parsing. CSLI Publicat… [find](https://scholar.google.com/scholar?q=Makino%2C+Takaki%2C+Yusuke+Miyao%2C+Kentaro+Torisawa%2C+and+Jun%E2%80%99ichi+Tsujii%2E+2002%2E+Native%2Dcode+compilation+of+feature+structures%2E+In+Stephen+Oepen%2C+Dan+Flickinger%2C+Jun%E2%80%99ichi+Tsujii%2C+and+Hans+Uszkoreit%2C+editors)
37. Malouf, Robert. 2002. A comparison of algorithms for maximum entropy parameter estimation. In Proceedings of the Sixth Conference on Natural Language Learning (CoNLL-2002), pages 1–7, Taipei. [find](https://scholar.google.com/scholar?q=Malouf%2C+Robert%2E+2002%2E+A+comparison+of+algorithms+for+maximum+entropy+parameter+estimation%2E+In+Proceedings+of+the+Sixth+Conference+on+Natural+Language+Learning+%28CoNLL%2D2002%29%2C+pages+1%E2%80%937%2C+Taipei%2E)
38. Malouf, Robert and Gertjan van Noord. 2004. Wide coverage parsing with stochastic attribute value grammars. In Proceedings of the IJCNLP-04 Workshop “Beyond Shallow Analyses”, Hainan Island. [find](https://scholar.google.com/scholar?q=Malouf%2C+Robert+and+Gertjan+van+Noord%2E+2004%2E+Wide+coverage+parsing+with+stochastic+attribute+value+grammars%2E+In+Proceedings+of+the+IJCNLP%2D04+Workshop+%E2%80%9CBeyond+Shallow+Analyses%E2%80%9D%2C+Hainan+Island%2E)
39. Marcus, Mitchell, Grace Kim, Mary Ann Marcinkiewicz, Robert MacIntyre, Ann Bies, Mark Ferguson, Karen Katz, and Britta Schasberger. 1994. The Penn Treebank: Annotating predicate argument structure. In Proceedings of the Workshop on Human Language Technology, pages 114–119, Plainsboro, NJ. [find](https://scholar.google.com/scholar?q=Marcus%2C+Mitchell%2C+Grace+Kim%2C+Mary+Ann+Marcinkiewicz%2C+Robert+MacIntyre%2C+Ann+Bies%2C+Mark+Ferguson%2C+Karen+Katz%2C+and+Britta+Schasberger%2E+1994%2E+The+Penn+Treebank%3A+Annotating+predicate+argument+structure%2E+In) [our card](https://md.mova.elamur.ai/free/en/docs/papers/bank-1994-marcus-penn-treebank-annotating-predicate-argument/)
40. Maxwell John T., III and Ronald M. Kaplan. 1995. A method for disjunctive constraint satisfaction. In Mary Dalrymple, Ronald M. Kaplan, John T. Maxwell, III, and Annie Zaenen, editors, Formal Issues in Lexical-Functional Grammar, number 47 in CSLI Lecture Notes Series. CSLI Publications, Palo Alto,… [find](https://scholar.google.com/scholar?q=Maxwell+John+T%2E%2C+III+and+Ronald+M%2E+Kaplan%2E+1995%2E+A+method+for+disjunctive+constraint+satisfaction%2E+In+Mary+Dalrymple%2C+Ronald+M%2E+Kaplan%2C+John+T%2E+Maxwell%2C+III%2C+and+Annie+Zaenen%2C+editors%2C+Formal+Issues+i)
41. McCallum, Andrew and Wei Li. 2003. Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In Proceedings of the 7th Conference on Natural Language Learning (CoNLL), pages 188–191, Edmonton. [find](https://scholar.google.com/scholar?q=McCallum%2C+Andrew+and+Wei+Li%2E+2003%2E+Early+results+for+named+entity+recognition+with+conditional+random+fields%2C+feature+induction+and+web%2Denhanced+lexicons%2E+In+Proceedings+of+the+7th+Conference+on+Natur)
42. McCallum, Andrew, Khashayar Rohanimanesh, and Charles Sutton. 2003. Dynamic conditional random fields for jointly labeling multiple sequences. In Proceedings of the Workshop on Syntax, Semantics, Statistics at the 16th Annual Conference on Neural Information Processing Systems, Vancouver. Available… [find](https://scholar.google.com/scholar?q=McCallum%2C+Andrew%2C+Khashayar+Rohanimanesh%2C+and+Charles+Sutton%2E+2003%2E+Dynamic+conditional+random+fields+for+jointly+labeling+multiple+sequences%2E+In+Proceedings+of+the+Workshop+on+Syntax%2C+Semantics%2C+Stat)
43. Miyao, Yusuke, Takashi Ninomiya, and Jun’ichi Tsujii. 2003. Probabilistic modeling of argument structures including non-local dependencies. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2003), pages 285–291, Borovets. [find](https://scholar.google.com/scholar?q=Miyao%2C+Yusuke%2C+Takashi+Ninomiya%2C+and+Jun%E2%80%99ichi+Tsujii%2E+2003%2E+Probabilistic+modeling+of+argument+structures+including+non%2Dlocal+dependencies%2E+In+Proceedings+of+the+International+Conference+on+Recent+Adv)
44. Miyao, Yusuke, Takashi Ninomiya, and Jun’ichi Tsujii. 2005. Corpus-oriented grammar development for acquiring a head-driven phrase structure grammar from the Penn Treebank. In Natural Language Processing - IJCNLP 2004, pages 684–693, Hainan Island. [find](https://scholar.google.com/scholar?q=Miyao%2C+Yusuke%2C+Takashi+Ninomiya%2C+and+Jun%E2%80%99ichi+Tsujii%2E+2005%2E+Corpus%2Doriented+grammar+development+for+acquiring+a+head%2Ddriven+phrase+structure+grammar+from+the+Penn+Treebank%2E+In+Natural+Language+Process)
45. Miyao, Yusuke, Tomoko Ohta, Katsuya Masuda, Yoshimasa Tsuruoka, Kazuhiro Yoshida, Takashi Ninomiya, and Jun’ichi Tsujii. 2006. Semantic retrieval for the accurate identification of relational concepts in massive textbases. In Proceedings of the Joint Conference of the 21st International Conference o… [find](https://scholar.google.com/scholar?q=Miyao%2C+Yusuke%2C+Tomoko+Ohta%2C+Katsuya+Masuda%2C+Yoshimasa+Tsuruoka%2C+Kazuhiro+Yoshida%2C+Takashi+Ninomiya%2C+and+Jun%E2%80%99ichi+Tsujii%2E+2006%2E+Semantic+retrieval+for+the+accurate+identification+of+relational+concepts)
46. Miyao, Yusuke and Jun’ichi Tsujii. 2002. Maximum entropy estimation for feature forests. In Proceedings of the Human Language Technology Conference (HLT-2002), pages 292–297, San Diego, CA. [find](https://scholar.google.com/scholar?q=Miyao%2C+Yusuke+and+Jun%E2%80%99ichi+Tsujii%2E+2002%2E+Maximum+entropy+estimation+for+feature+forests%2E+In+Proceedings+of+the+Human+Language+Technology+Conference+%28HLT%2D2002%29%2C+pages+292%E2%80%93297%2C+San+Diego%2C+CA%2E)
47. Miyao, Yusuke and Jun’ichi Tsujii. 2003. A model of syntactic disambiguation based on lexicalized grammars. In Proceedings of the Seventh Conference on Computational Natural Language Learning (CoNLL-2003), pages 1–8, Edmonton. [find](https://scholar.google.com/scholar?q=Miyao%2C+Yusuke+and+Jun%E2%80%99ichi+Tsujii%2E+2003%2E+A+model+of+syntactic+disambiguation+based+on+lexicalized+grammars%2E+In+Proceedings+of+the+Seventh+Conference+on+Computational+Natural+Language+Learning+%28CoNLL%2D2)
48. Miyao, Yusuke and Jun’ichi Tsujii. 2005. Probabilistic disambiguation models for wide-coverage HPSG parsing. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pages 83–90, Ann Arbor, MI. [find](https://scholar.google.com/scholar?q=Miyao%2C+Yusuke+and+Jun%E2%80%99ichi+Tsujii%2E+2005%2E+Probabilistic+disambiguation+models+for+wide%2Dcoverage+HPSG+parsing%2E+In+Proceedings+of+the+43rd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+) [our card](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2005-miyao-probabilistic-disambiguation-models-wide-coverage/)
49. Nakanishi, Hiroko, Yusuke Miyao, and Jun’ichi Tsujii. 2005. Probabilistic models for disambiguation of an HPSG-based chart generator. In Proceedings of the 9th International Workshop on Parsing Technologies (IWPT 2005), pages 93–102, Vancouver. [find](https://scholar.google.com/scholar?q=Nakanishi%2C+Hiroko%2C+Yusuke+Miyao%2C+and+Jun%E2%80%99ichi+Tsujii%2E+2005%2E+Probabilistic+models+for+disambiguation+of+an+HPSG%2Dbased+chart+generator%2E+In+Proceedings+of+the+9th+International+Workshop+on+Parsing+Techno)
50. Ninomiya, Takashi, Yoshimasa Tsuruoka, Yusuke Miyao, and Jun’ichi Tsujii. 2005. Efficacy of beam thresholding, unification filtering and hybrid parsing in probabilistic HPSG parsing. In Proceedings of the 9th International Workshop on Parsing Technologies, pages 103–114, Vancouver. [find](https://scholar.google.com/scholar?q=Ninomiya%2C+Takashi%2C+Yoshimasa+Tsuruoka%2C+Yusuke+Miyao%2C+and+Jun%E2%80%99ichi+Tsujii%2E+2005%2E+Efficacy+of+beam+thresholding%2C+unification+filtering+and+hybrid+parsing+in+probabilistic+HPSG+parsing%2E+In+Proceedings+of)
51. Nocedal, Jorge. 1980. Updating quasi-Newton matrices with limited storage. Mathematics of Computation, 35:773–782. [find](https://scholar.google.com/scholar?q=Nocedal%2C+Jorge%2E+1980%2E+Updating+quasi%2DNewton+matrices+with+limited+storage%2E+Mathematics+of+Computation%2C+35%3A773%E2%80%93782%2E)
52. Nocedal, Jorge and Stephen J. Wright. 1999. Numerical Optimization. Springer, New York. [find](https://scholar.google.com/scholar?q=Nocedal%2C+Jorge+and+Stephen+J%2E+Wright%2E+1999%2E+Numerical+Optimization%2E+Springer%2C+New+York%2E)
53. Oepen, Stephan and John Carroll. 2000. Ambiguity packing in constraint-based parsing: practical results. In Proceedings of the First Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2000), pages 162–169, Seattle, WA. [find](https://scholar.google.com/scholar?q=Oepen%2C+Stephan+and+John+Carroll%2E+2000%2E+Ambiguity+packing+in+constraint%2Dbased+parsing%3A+practical+results%2E+In+Proceedings+of+the+First+Conference+of+the+North+American+Chapter+of+the+Association+for+Com)
54. Oepen, Stephan, Dan Flickinger, Jun’ichi Tsujii, and Hans Uszkoreit, editors. 2002. Collaborative Language Engineering: A Case Study in Efficient Grammar-Based Processing. CSLI Publications, Palo Alto, CA. [find](https://scholar.google.com/scholar?q=Oepen%2C+Stephan%2C+Dan+Flickinger%2C+Jun%E2%80%99ichi+Tsujii%2C+and+Hans+Uszkoreit%2C+editors%2E+2002%2E+Collaborative+Language+Engineering%3A+A+Case+Study+in+Efficient+Grammar%2DBased+Processing%2E+CSLI+Publications%2C+Palo+Alto)
55. Oepen, Stephan, Kristina Toutanova, Stuart Shieber, Christopher Manning, Dan Flickinger, and Thorsten Brants. 2002. The LinGO Redwoods treebank motivation and preliminary applications. In Proceedings of the 19th International Conference on Computational Linguistics (COLING 2002), volume 2, pages 1–5… [find](https://scholar.google.com/scholar?q=Oepen%2C+Stephan%2C+Kristina+Toutanova%2C+Stuart+Shieber%2C+Christopher+Manning%2C+Dan+Flickinger%2C+and+Thorsten+Brants%2E+2002%2E+The+LinGO+Redwoods+treebank+motivation+and+preliminary+applications%2E+In+Proceedings+) [our card](https://md.mova.elamur.ai/free/en/docs/papers/bank-2002-oepen-lingo-redwoods-treebank-motivation-preliminary/)
56. Osborne, Miles. 2000. Estimation of stochastic attribute-value grammar using an informative sample. In Proceedings of the 18th International Conference on Computational Linguistics (COLING 2000), volume 1, pages 586–592, Saarbrücken. [find](https://scholar.google.com/scholar?q=Osborne%2C+Miles%2E+2000%2E+Estimation+of+stochastic+attribute%2Dvalue+grammar+using+an+informative+sample%2E+In+Proceedings+of+the+18th+International+Conference+on+Computational+Linguistics+%28COLING+2000%29%2C+volu)
57. Peng, Fuchun and Andrew McCallum. 2004. Accurate information extraction from research papers using conditional random fields. In Proceedings of Human Language Technology Conference and North American Chapter of the Association for Computational Linguistics (HLT/ NAACL-04), pages 329–336, Boston, MA. [find](https://scholar.google.com/scholar?q=Peng%2C+Fuchun+and+Andrew+McCallum%2E+2004%2E+Accurate+information+extraction+from+research+papers+using+conditional+random+fields%2E+In+Proceedings+of+Human+Language+Technology+Conference+and+North+American+)
58. Pinto, David, Andrew McCallum, Xen Lee, and W. Bruce Croft. 2003. Table extraction using conditional random fields. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2003), pages 235–242, Toronto. [find](https://scholar.google.com/scholar?q=Pinto%2C+David%2C+Andrew+McCallum%2C+Xen+Lee%2C+and+W%2E+Bruce+Croft%2E+2003%2E+Table+extraction+using+conditional+random+fields%2E+In+Proceedings+of+the+26th+Annual+International+ACM+SIGIR+Conference+on+Research+and)
59. Pollard, Carl and Ivan A. Sag. 1994. Head-Driven Phrase Structure Grammar. University of Chicago Press, Chicago, IL. [find](https://scholar.google.com/scholar?q=Pollard%2C+Carl+and+Ivan+A%2E+Sag%2E+1994%2E+Head%2DDriven+Phrase+Structure+Grammar%2E+University+of+Chicago+Press%2C+Chicago%2C+IL%2E)
60. Riezler, Stefan, Tracy H. King, Ronald M. Kaplan, Richard Crouch, John T. Maxwell, III, and Mark Johnson. 2002. Parsing the Wall Street Journal using a lexical-functional grammar and discriminative estimation techniques. In Proceedings of the 40th Annual Meeting of the Association for Computational… [find](https://scholar.google.com/scholar?q=Riezler%2C+Stefan%2C+Tracy+H%2E+King%2C+Ronald+M%2E+Kaplan%2C+Richard+Crouch%2C+John+T%2E+Maxwell%2C+III%2C+and+Mark+Johnson%2E+2002%2E+Parsing+the+Wall+Street+Journal+using+a+lexical%2Dfunctional+grammar+and+discriminative+es)
61. Riezler, Stefan, Detlef Prescher, Jonas Kuhn, and Mark Johnson. 2000. Lexicalized stochastic modeling of constraint-based grammars using log-linear measures and EM training. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics (ACL 2000), pages 480–487, Hong Kon… [find](https://scholar.google.com/scholar?q=Riezler%2C+Stefan%2C+Detlef+Prescher%2C+Jonas+Kuhn%2C+and+Mark+Johnson%2E+2000%2E+Lexicalized+stochastic+modeling+of+constraint%2Dbased+grammars+using+log%2Dlinear+measures+and+EM+training%2E+In+Proceedings+of+the+38th)
62. Riezler, Stefan and Alexander Vasserman. 2004. Incremental feature selection and l 1 regularization for relaxed maximum-entropy modeling. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), pages 174–181, Barcelona. [find](https://scholar.google.com/scholar?q=Riezler%2C+Stefan+and+Alexander+Vasserman%2E+2004%2E+Incremental+feature+selection+and+l+1+regularization+for+relaxed+maximum%2Dentropy+modeling%2E+In+Proceedings+of+the+2004+Conference+on+Empirical+Methods+in+)
63. Roark, Brian, Murat Saraclar, Michael Collins, and Mark Johnson. 2004. Discriminative language modeling with conditional random fields and the perceptron algorithm. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004), pages 47–54, Barcelona. [find](https://scholar.google.com/scholar?q=Roark%2C+Brian%2C+Murat+Saraclar%2C+Michael+Collins%2C+and+Mark+Johnson%2E+2004%2E+Discriminative+language+modeling+with+conditional+random+fields+and+the+perceptron+algorithm%2E+In+Proceedings+of+the+42nd+Annual+M)
64. Rosenfeld, Ronald. 1997. A whole sentence maximum entropy language model. In Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding, pages 230–237, Santa Barbara, CA. [find](https://scholar.google.com/scholar?q=Rosenfeld%2C+Ronald%2E+1997%2E+A+whole+sentence+maximum+entropy+language+model%2E+In+Proceedings+of+the+IEEE+Workshop+on+Automatic+Speech+Recognition+and+Understanding%2C+pages+230%E2%80%93237%2C+Santa+Barbara%2C+CA%2E)
65. Sag, Ivan A., Thomas Wasow, and Emily M. Bender. 2003. Syntactic Theory: A Formal Introduction. Number 152 in CSLI Lecture Notes. CSLI Publications, Standford, CA. [find](https://scholar.google.com/scholar?q=Sag%2C+Ivan+A%2E%2C+Thomas+Wasow%2C+and+Emily+M%2E+Bender%2E+2003%2E+Syntactic+Theory%3A+A+Formal+Introduction%2E+Number+152+in+CSLI+Lecture+Notes%2E+CSLI+Publications%2C+Standford%2C+CA%2E)
66. Sarawagi, Sunita and William W. Cohen. 2004. Semi-Markov conditional random fields for information extraction. In Proceedings of the 18th Annual Conference on Neural Information Processing Systems, pages 1185–1192, Vancouver. [find](https://scholar.google.com/scholar?q=Sarawagi%2C+Sunita+and+William+W%2E+Cohen%2E+2004%2E+Semi%2DMarkov+conditional+random+fields+for+information+extraction%2E+In+Proceedings+of+the+18th+Annual+Conference+on+Neural+Information+Processing+Systems%2C+pa)
67. Sato, Taisuke. 2005. A generic approach to em learning for symbolic-statistical models. In Proceedings of the 4th Learning Language in Logic Workshop (LLL05), pages 21–28, Bonn. [find](https://scholar.google.com/scholar?q=Sato%2C+Taisuke%2E+2005%2E+A+generic+approach+to+em+learning+for+symbolic%2Dstatistical+models%2E+In+Proceedings+of+the+4th+Learning+Language+in+Logic+Workshop+%28LLL05%29%2C+pages+21%E2%80%9328%2C+Bonn%2E)
68. Sato, Taisuke and Yoshitaka Kameya. 1997. PRISM: a language for symbolic-statistical modeling. In Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI ’97), pages 1330–1335, Nagoya. [find](https://scholar.google.com/scholar?q=Sato%2C+Taisuke+and+Yoshitaka+Kameya%2E+1997%2E+PRISM%3A+a+language+for+symbolic%2Dstatistical+modeling%2E+In+Proceedings+of+the+15th+International+Joint+Conference+on+Artificial+Intelligence+%28IJCAI+%E2%80%9997%29%2C+pages+1)
69. Sato, Taisuke and Yoshitaka Kameya. 2001. Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research, 15:391–454. [find](https://scholar.google.com/scholar?q=Sato%2C+Taisuke+and+Yoshitaka+Kameya%2E+2001%2E+Parameter+learning+of+logic+programs+for+symbolic%2Dstatistical+modeling%2E+Journal+of+Artificial+Intelligence+Research%2C+15%3A391%E2%80%93454%2E)
70. Settles, Burr. 2004. Biomedical named entity recognition using conditional random fields and rich feature sets. In Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA), pages 104–107, Geneva. [find](https://scholar.google.com/scholar?q=Settles%2C+Burr%2E+2004%2E+Biomedical+named+entity+recognition+using+conditional+random+fields+and+rich+feature+sets%2E+In+Proceedings+of+the+International+Joint+Workshop+on+Natural+Language+Processing+in+Bio)
71. Sha, Fei and Fernando Pereira. 2003. Shallow parsing with conditional random fields. In Proceedings of the 2003 Human Language Technology Conference and North American Chapter of the Association for Computational Linguistics (HLT-NAACL 2003), pages 213–220, Edmonton. [find](https://scholar.google.com/scholar?q=Sha%2C+Fei+and+Fernando+Pereira%2E+2003%2E+Shallow+parsing+with+conditional+random+fields%2E+In+Proceedings+of+the+2003+Human+Language+Technology+Conference+and+North+American+Chapter+of+the+Association+for+C)
72. Shieber, Stuart M. 1985. Using restriction to extend parsing algorithms for complex-feature-based formalisms. In Proceedings of the 23rd Annual Meeting on Association for Computational Linguistics, pages 145–152, Chicago, IL. [find](https://scholar.google.com/scholar?q=Shieber%2C+Stuart+M%2E+1985%2E+Using+restriction+to+extend+parsing+algorithms+for+complex%2Dfeature%2Dbased+formalisms%2E+In+Proceedings+of+the+23rd+Annual+Meeting+on+Association+for+Computational+Linguistics%2C+pa)
73. Sutton, Charles, Khashayar Rohanimanesh, and Andrew McCallum. 2004. Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data. In Proceedings of the 21st International Conference on Machine Learning (ICML 2004), pages 783–790, Alberta. [find](https://scholar.google.com/scholar?q=Sutton%2C+Charles%2C+Khashayar+Rohanimanesh%2C+and+Andrew+McCallum%2E+2004%2E+Dynamic+conditional+random+fields%3A+Factorized+probabilistic+models+for+labeling+and+segmenting+sequence+data%2E+In+Proceedings+of+the+)
74. Taskar, Ben, Dan Klein, Michael Collins, Daphne Koller, and Chris Manning. 2004. Max-margin parsing. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), pages 1–8, Barcelona. [find](https://scholar.google.com/scholar?q=Taskar%2C+Ben%2C+Dan+Klein%2C+Michael+Collins%2C+Daphne+Koller%2C+and+Chris+Manning%2E+2004%2E+Max%2Dmargin+parsing%2E+In+Proceedings+of+the+2004+Conference+on+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP+20)
75. Toutanova, Kristina and Christopher Manning. 2002. Feature selection for a rich HPSG grammar using decision trees. In Proceedings of the Sixth Conference on Natural Language Lerning (CoNLL-2002), pages 77–83, Taipei. [find](https://scholar.google.com/scholar?q=Toutanova%2C+Kristina+and+Christopher+Manning%2E+2002%2E+Feature+selection+for+a+rich+HPSG+grammar+using+decision+trees%2E+In+Proceedings+of+the+Sixth+Conference+on+Natural+Language+Lerning+%28CoNLL%2D2002%29%2C+page)
76. Toutanova, Kristina, Penka Markova, and Christopher Manning. 2004. The leaf projection path view of parse trees: Exploring string kernels for HPSG parse selection. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), pages 166–173, Barcelona. [find](https://scholar.google.com/scholar?q=Toutanova%2C+Kristina%2C+Penka+Markova%2C+and+Christopher+Manning%2E+2004%2E+The+leaf+projection+path+view+of+parse+trees%3A+Exploring+string+kernels+for+HPSG+parse+selection%2E+In+Proceedings+of+the+2004+Conferenc)
77. Tsujii Laboratory. 2004. Enju—A practical HPSG parser. Available at http://www. tsujii.is.s.u-tokyo.ac.jp/enju/. [find](https://scholar.google.com/scholar?q=Tsujii+Laboratory%2E+2004%2E+Enju%E2%80%94A+practical+HPSG+parser%2E+Available+at+http%3A%2F%2Fwww%2E+tsujii%2Eis%2Es%2Eu%2Dtokyo%2Eac%2Ejp%2Fenju%2F%2E)
78. Tsuruoka, Yoshimasa, Yusuke Miyao, and Jun’ichi Tsujii. 2004. Towards efficient probabilistic HPSG parsing: Integrating semantic and syntatic preference to guide the parsing. In Proceedings of the IJCNLP-04 Workshop “Beyond Shallow Analyses”, Hainan Island. Available at www.tsujii.is.s.u-tokyo.ac.jp… [find](https://scholar.google.com/scholar?q=Tsuruoka%2C+Yoshimasa%2C+Yusuke+Miyao%2C+and+Jun%E2%80%99ichi+Tsujii%2E+2004%2E+Towards+efficient+probabilistic+HPSG+parsing%3A+Integrating+semantic+and+syntatic+preference+to+guide+the+parsing%2E+In+Proceedings+of+the+IJC)
79. Tsuruoka, Yoshimasa and Jun’ichi Tsujii. 2005. Bidirectional inference with the easiest-first strategy for tagging sequence data. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP 2005), pages 467–474, Vancouver. [find](https://scholar.google.com/scholar?q=Tsuruoka%2C+Yoshimasa+and+Jun%E2%80%99ichi+Tsujii%2E+2005%2E+Bidirectional+inference+with+the+easiest%2Dfirst+strategy+for+tagging+sequence+data%2E+In+Proceedings+of+Human+Language+Technology+Conference+and+Conference+)
80. Vapnik, Vladimir N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York. [find](https://scholar.google.com/scholar?q=Vapnik%2C+Vladimir+N%2E+1995%2E+The+Nature+of+Statistical+Learning+Theory%2E+Springer%2DVerlag%2C+New+York%2E)
81. Vijay-Shanker, K., David J. Weir, and Aravind K. Joshi. 1987. Characterizing structural descriptions produced by various grammatical formalisms. In Proceedings of the 25th Annual Meeting of the Association for Computational Linguistics, pages 104–111, Palo Alto, CA. [find](https://scholar.google.com/scholar?q=Vijay%2DShanker%2C+K%2E%2C+David+J%2E+Weir%2C+and+Aravind+K%2E+Joshi%2E+1987%2E+Characterizing+structural+descriptions+produced+by+various+grammatical+formalisms%2E+In+Proceedings+of+the+25th+Annual+Meeting+of+the+Associ)
82. Weir, David J. 1988. Characterizing Mildly Context-Sensitive Grammar Formalisms. Ph.D. thesis, University of Pennsylvania. [find](https://scholar.google.com/scholar?q=Weir%2C+David+J%2E+1988%2E+Characterizing+Mildly+Context%2DSensitive+Grammar+Formalisms%2E+Ph%2ED%2E+thesis%2C+University+of+Pennsylvania%2E)
83. Yakushiji, Akane, Yusuke Miyao, Tomoko Ohta, Yuka Tateisi, and Jun’ichi Tsujii. 2006. Automatic construction of predicate-argument structure patterns for biomedical information extraction. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 2… [find](https://scholar.google.com/scholar?q=Yakushiji%2C+Akane%2C+Yusuke+Miyao%2C+Tomoko+Ohta%2C+Yuka+Tateisi%2C+and+Jun%E2%80%99ichi+Tsujii%2E+2006%2E+Automatic+construction+of+predicate%2Dargument+structure+patterns+for+biomedical+information+extraction%2E+In+Proceedi)