Learning distributed representations for structured output prediction

Advances in Neural Information Processing Systems

Volumn 4, Issue January, 2014, Pages 3266-3274

  Srikumar, Vivek ^a   Manning, Christopher D ^b  

^a UNIVERSITY OF UTAH   (United States)

^b STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; FORECASTING; INFORMATION RETRIEVAL SYSTEMS; INFORMATION SCIENCE; TENSORS;

ALTERNATING MINIMIZATION ALGORITHMS; DISTRIBUTED REPRESENTATION; DOCUMENT CLASSIFICATION; NATURAL GENERALIZATION; PART OF SPEECH TAGGING; STATISTICAL INFORMATION; STRUCTURED OUTPUT PREDICTION; STRUCTURED PREDICTION;

LEARNING ALGORITHMS;

EID: 84937874740     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. J. Abernethy, F. Bach, T. Evgeniou, and J. Vert. Low-rank matrix factorization with attributes. arXiv preprint cs/0611124, 2006.
2. Y. Amit, M. Fink, N. Srebro, and S. Ullman. Uncovering shared structures in multiclass classification. In International Conference on Machine Learning, 2007.
3. A. Argyriou, T. Evgeniou, and M. Pontil. Multi-task feature learning. Advances in Neural Information Processing Systems, 2007.
4. Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013.
5. T. Brants. TnT: a statistical part-of-speech tagger. In Conference on Applied Natural Language Processing, 2000.
6. N. Cesa-Bianchi, C. Gentile, and L. Zaniboni. Hierarchical classification: combining bayes with svm. In International Conference on Machine learning, 2006.
7. A. Coates, A. Ng, and H. Lee. An analysis of single-layer networks in unsupervised feature learning. In International Conference on Artificial Intelligence and Statistics, 2011.
8. M. Collins. Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms. In Conference on Empirical Methods in Natural Language Processing, 2002.
9. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa. Natural language processing (almost) from scratch. Journal for Machine Learning Research, 12, 2011.
10. M. Fazel, H. Hindi, and S. Boyd. Rank minimization and applications in system theory. In Proceedings of the American Control Conference, Volume 4, 2004.
11. G. E. Hinton. Representing part-whole hierarchies in connectionist networks. In Annual Conference of the Cognitive Science Society, 1988.
12. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Machine Learning, 2001.
13. K. Lang. Newsweeder: Learning to filter netnews. In International Conference on Machine Learning, 1995.
14. T. Lei, Y. Xin, Y. Zhang, R. Barzilay, and T. Jaakkola. Low-rank tensors for scoring dependency structures. In Annual Meeting of the Association for Computational Linguistics, 2014.
15. M. Marcus, G. Kim, M. Marcinkiewicz, R. MacIntyre, A. Bies, M. Ferguson, K. Katz, and B. Schasberger. The Penn Treebank: Annotating Predicate Argument Structure. In Workshop on Human Language Technology, 1994.
16. T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv: 1301.3781, 2013.
17. J. Nivre, J. Hall, S. Kübler, R. McDonald, J. Nilsson, S. Riedel, and D. Yuret. The CoNLL 2007 shared task on dependency parsing. In CoNLL shared task session of EMNLP-CoNLL, 2007.
18. N. Parikh and S. Boyd. Proximal algorithms. Foundations and Trends in optimization, 1(3), 2013.
19. S. Petrov, D. Das, and R. McDonald. A universal part-of-speech tagset. arXiv preprint arXiv: 1104.2086, 2011.
20. T. A. Plate. Holographic reduced representations. IEEE Transactions on Neural Networks, 6(3), 1995.
21. P. Smolensky. Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial intelligence, 46(1), 1990.
22. R. Socher, B. Huval, C. Manning, and A. Ng. Semantic Compositionality Through Recursive Matrix-Vector Spaces. In Empirical Methods in Natural Language Processing, 2012.
23. N. Srebro, J. Rennie, and T. Jaakkola. Maximum-margin matrix factorization. In Advances in Neural Information Processing Systems, 2004.
24. K. Toutanova, D. Klein, C. Manning, and Y. Singer. Feature-rich part-of-speech tagging with a cyclic dependency network. In Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, 2003.
25. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. Journal for Machine Learning Research, 2005.
26. J. Turian, L. Ratinov, and Y. Bengio. Word Representations: A Simple and General Method for Semi-Supervised Learning. In Annual Meeting of the Association for Computational Linguistics, 2010.
27. L. Vandenberghe and S. Boyd. Semidefinite programming. SIAM review, 38(1), 1996.