Spectral dependency parsing with latent variables

EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference

Volumn , Issue , 2012, Pages 205-213

  Dhillon, Paramveer S ^a   Rodu, Jordan ^b   Collins, Michael ^c   Foster, Dean P ^b   Ungar, Lyle H ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c NONE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DEPENDENCY PARSING; DEPENDENCY TREES; EXPERIMENTAL EVALUATION; GENERATIVE MODEL; MODEL PARAMETERS; SPECTRAL DEPENDENCY; SPECTRAL LEARNING; SPECTRAL METHODS;

EQUIVALENCE CLASSES; FORESTRY; LEARNING ALGORITHMS; SPECTROSCOPY; TREES (MATHEMATICS);

NATURAL LANGUAGE PROCESSING SYSTEMS;

ALGORITHMS; DATA PROCESSING; FORESTRY; PROGRAMING LANGUAGES;

EID: 84883407142     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Shay Cohen, Karl Stratos, Michael Collins, Dean Foster, and Lyle Ungar. Spectral learning of latent-variable pcfgs. In Association of Computational Linguistics (ACL), Volume 50, 2012.
2. Michael Collins. Ranking algorithms for named-entity extraction: boosting and the voted percep-tron. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL '02, pages 489-496, Stroudsburg, PA, USA, 2002. Association for Computational Linguistics. URL Http://dx.doi.org/10.3115/1073083.1073165.
3. Michael Collins and Terry Koo. Discriminative reranking for natural language parsing. Comput. Linguist., 31(1):25-70, March 2005. ISSN 0891-2017.
4. Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, and Yoram Singer. Online passive-aggressive algorithms. Journal of Machine Learning Research, 7:551-585, 2006.
5. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. JRSS, SERIES B, 39(1):1-38, 1977.
6. Paramveer S. Dhillon, Dean Foster, and Lyle Ungar. Multi-view learning of word embeddings via cca. In Advances in Neural Information Processing Systems (NIPS), Volume 24, 2011.
7. Jason Eisner and Giorgio Satta. Efficient parsing for bilexical context-free grammars and head-automaton grammars. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL), pages 457-464, University of Maryland, June 1999. URL Http://cs.jhu.edu/~jason/papers/ #acl99.
8. Dean Foster, Jordan Rodu, and Lyle Ungar. Spectral dimensionality reduction for HMMs. ArXiV Http://arxiv.org/abs/1203.6130, 2012.
9. D Hsu, S M. Kakade, and Tong Zhang. A spectral algorithm for learning hidden markov models. arXiv:0811.4413v2, 2008.
10. Terry Koo, Xavier Carreras, and Michael Collins. Simple semi-supervised dependency parsing. In Proc. ACL/HLT, 2008.
11. F. Luque, A. Quattoni, B. Balle, and X. Carreras. Spectral learning for non-deterministic dependency parsing. In EACL, 2012.
12. Mitchell P. Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. Building a large annotated corpus of english: the penn treebank. Comput. Linguist., 19:313-330, June 1993. ISSN 0891-2017.
13. Ryan McDonald. Discriminative learning and spanning tree algorithms for dependency parsing. PhD thesis, University of Pennsylvania, Philadelphia, PA, USA, 2006. AAI3225503.
14. Bernard Merialdo. Tagging english text with a probabilistic model. Comput. Linguist., 20:155-171, June 1994. ISSN 0891-2017.
15. Gabriele Antonio Musillo and Paola Merlo. Un-lexicalised hidden variable models of split dependency grammars. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers, HLT-Short '08, pages 213-216, Stroudsburg, PA, USA, 2008. Association for Computational Linguistics.
16. Ankur P. Parikh, Le Song, and Eric P. Xing. A spectral algorithm for latent tree graphical models. In ICML, pages 1065-1072, 2011.
17. Slav Petrov, Leon Barrett, Romain Thibaux, and Dan Klein. Learning accurate, compact, and in-terpretable tree annotation. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, ACL-44, pages 433-440, Stroudsburg, PA, USA, 2006. Association for Computational Linguistics.
18. Ariadna Quattoni, Michael Collins, and Trevor Darrell. Conditional random fields for object recognition. In In NIPS, pages 1097-1104. MIT Press, 2004.
19. Lawrence R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. In Proceedings of the IEEE, pages 257-286, 1989.
20. Adwait Ratnaparkhi. A Maximum Entropy Model for Part-Of-Speech Tagging. In Eric Brill and Kenneth Church, editors, Proceedings of the Empirical Methods in Natural Language Processing, pages 133-142, 1996.
21. Sebastiaan Terwijn. On the learnability of hidden markov models. In Proceedings of the 6th International Colloquium on Grammatical Inference: Algorithms and Applications, ICGI '02, pages 261-268, London, UK, UK, 2002. Springer-Verlag. ISBN 3-540-44239-1.