Spectral unsupervised parsing with additive tree metrics

52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference

Volumn 1, Issue , 2014, Pages 1062-1072

  Parikh, Ankur P ^a   Cohen, Shay B ^b   Xing, Eric P ^a  

^a Carnegie Mellon University   (United States)

^b UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ADDITIVES; COMPUTATIONAL LINGUISTICS; FORESTRY; MACHINE LEARNING; SYNTACTICS;

CONTEXT MODELING; LATENT STRUCTURES; MACHINE LEARNING COMMUNITIES; PARSING ALGORITHM; STRUCTURE-LEARNING; THEORETICAL GUARANTEES; TREE METRICS; TREE STRUCTURES;

TREES (MATHEMATICS);

FORESTRY; MATHEMATICS; TREES;

EID: 84906927796     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/v1/p14-1100     Document Type: Conference Paper

References (42)

1. A. Anandkumar, K. Chaudhuri, D. Hsu, S. M. Kakade, L. Song, and T. Zhang. 2011. Spectral methods for learning multivariate latent tree structure. arXiv preprint arXiv:1107.1283.
2. R. Bailly, F. Denis, and L. Ralaivola. 2009. Grammatical inference as a principal component analysis problem. In Proceedings of ICML.
3. R. Bailly, X. Carreras, F. M. Luque, and A. Quattoni. 2013. Unsupervised spectral learning of WCFG as low-rank matrix completion. In Proceedings of EMNLP.
4. P. F. Brown, P.V. Desouza, R.L. Mercer, V.J.D. Pietra, and J.C. Lai. 1992. Class-based n-gram models of natural language. Computational linguistics, 18(4):467-479.
5. O. P. Buneman. 1971. The recovery of trees from measures of dissimilarity. Mathematics in the archaeological and historical sciences.
6. P. Buneman. 1974. A note on the metric properties of trees. Journal of Combinatorial Theory, Series B, 17(1):48-50.
7. M.J. Choi, V. YF Tan, A. Anandkumar, and A.S. Willsky. 2011. Learning latent tree graphical models. The Journal of Machine Learning Research, 12:1771-1812.
8. C. K. Chow and C. N. Liu. 1968. Approximating Discrete Probability DistributionsWith Dependence Trees. IEEE Transactions on Information Theory, IT-14:462-467.
9. S. B. Cohen and N. A. Smith. 2009. Shared logistic normal distributions for soft parameter tying in unsupervised grammar induction. In Proceedings of HLT-NAACL.
10. S. B. Cohen and N. A. Smith. 2012. Empirical risk minimization for probabilistic grammars: Sample complexity and hardness of learning. Computational Linguistics, 38(3):479-526.
11. S. B. Cohen, K. Stratos, M. Collins, D. P. Foster, and L. Ungar. 2012. Spectral learning of latent-variable PCFGs. In Proceedings of ACL.
12. R. Desper and O. Gascuel. 2005. The minimum evolution distance-based approach to phylogenetic inference. Mathematics of evolution and phylogeny, pages 1-32.
13. P. S. Dhillon, J. Rodu, D. P. Foster, and L. H. Ungar. 2012. Two step cca: A new spectral method for estimating vector models of words. In Proceedings of ICML.
14. J. Eisner and G. Satta. 1999. Efficient parsing for bilexical context-free grammars and head automaton grammars. In Proceedings of ACL.
15. P. Erdõs, M. Steel, L. Székely, and T. Warnow. 1999. A few logs suffice to build (almost) all trees: Part ii. Theoretical Computer Science, 221(1):77-118.
16. J. Gillenwater, K. Ganchev, J. Graça, F. Pereira, and B. Taskar. 2010. Sparsity in dependency grammar induction. In Proceedings of ACL.
17. K. Gimpel and N.A. Smith. 2012. Concavity and initialization for unsupervised dependency parsing. In Proceedings of NAACL.
18. D. Golland, J. DeNero, and J. Uszkoreit. 2012. A feature-rich constituent context model for grammar induction. In Proceedings of ACL.
19. M. Gormley and J. Eisner. 2013. Nonconvex global optimization for latent-variable models. In Proceedings of ACL.
20. S. Harmeling and C. KI Williams. 2011. Greedy learning of binary latent trees. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(6):1087-1097.
21. T. Hastie, R. Tibshirani, and J. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer Verlag.
22. W. P. Headden, M. Johnson, and D. McClosky. 2009. Improving unsupervised dependency parsing with richer contexts and smoothing. In Proceedings of NAACL-HLT.
23. D. Hsu, S. Kakade, and T. Zhang. 2009. A spectral algorithm for learning hidden Markov models. In Proceedings of COLT.
24. D. Hsu, S. M. Kakade, and P. Liang. 2012. Identifiability and unmixing of latent parse trees. arXiv preprint arXiv:1206.3137.
25. M. Ishteva, H. Park, and L. Song. 2012. Unfolding latent tree structures using 4th order tensors. arXiv preprint arXiv:1210.1258.
26. F. Jelinek, J. D. Lafferty, and R. L. Mercer. 1992. Basic methods of probabilistic context free grammars. Springer.
27. D. Klein and C. D. Manning. 2002. A generative constituent-context model for improved grammar induction. In Proceedings of ACL.
28. M. Kolar, A. P. Parikh, and E. P. Xing. 2010a. On sparse nonparametric conditional covariance selection. In Proceedings of ICML.
29. M. Kolar, L. Song, A. Ahmed, and E. P. Xing. 2010b. Estimating time-varying networks. The Annals of Applied Statistics, 4(1):94-123.
30. M. P. Marcus, B. Santorini, and M. A. Marcinkiewicz. 1993. Building a large annotated corpus of English: The Penn treebank. Computational Linguistics, 19:313-330.
31. A.P. Parikh, L. Song, and E.P. Xing. 2011. A spectral algorithm for latent tree graphical models. In Proceedings of ICML.
32. S. Petrov, D. Das, and R. McDonald. 2011. A universal part-of-speech tagset. ArXiv:1104.2086.
33. A. Rzhetsky and M. Nei. 1993. Theoretical foundation of the minimum-evolution method of phylogenetic inference. Molecular Biology and Evolution, 10(5):1073-1095.
34. N. Saitou and M. Nei. 1987. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Molecular biology and evolution, 4(4):406-425.
35. Y. Seginer. 2007. Fast unsupervised incremental parsing. In Proceedings of ACL.
36. N. A. Smith and J. Eisner. 2005. Contrastive estimation: Training log-linear models on unlabeled data. In Proceedings of ACL.
37. L. Song, A.P. Parikh, and E.P. Xing. 2011. Kernel embeddings of latent tree graphical models. In Proceedings of NIPS.
38. V. I. Spitkovsky, H. Alshawi, and D. Jurafsky. 2010a. From baby steps to leapfrog: how less is more in unsupervised dependency parsing. In Proceedings of NAACL.
39. V. I. Spitkovsky, H. Alshawi, D. Jurafsky, and C. D. Manning. 2010b. Viterbi training improves unsupervised dependency parsing. In Proceedings of CoNLL.
40. V. I. Spitkovsky, H. Alshawi, and D. Jurafsky. 2013. Breaking out of local optima with count transforms and model recombination: A study in grammar induction. In Proceedings of EMNLP.
41. J. P. Turian, L.-A. Ratinov, and Y. Bengio. 2010. Word representations: A simple and general method for semi-supervised learning. In Proceedings of ACL.
42. S. Zhou, J. Lafferty, and L. Wasserman. 2010. Time varying undirected graphs. Machine Learning, 80(2-3):295-319.