Reproducing and learning new algebraic operations on word embeddings using genetic programming
- Published: 18 Feb 2017
[1] Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin. A neural probabilistic language model. Journal of machine learning research, 3(Feb):1137-1155, 2003.
[2] U. Bhowan, M. Johnston, M. Zhang, and X. Yao. Evolving diverse ensembles using genetic programming for classification with unbalanced data. IEEE Transactions on Evolutionary Computation, 17(3):368-386, 2013.
[3] W. Blacoe and M. Lapata. A comparison of vector-based representations for semantic composition. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 546-556. Association for Computational Linguistics, 2012.
[4] P. A. Bosman and E. D. De Jong. Learning probabilistic tree grammars for genetic programming. In International Conference on Parallel Problem Solving from Nature, pages 192-201. Springer, 2004.
[6] R. Cummins and C. O'Riordan. An analysis of the solution space for genetically programmed term-weighting schemes in information retrieval. In P. S. P. M. D. Bell, editor, 17th Artificial Intelligence and Cognitive Science Conference (AICS 2006), Queen's University, Belfast, 2006.
[7] H. J. Escalante, M. A. Garc´ıa-Limo´ n, A. Morales-Reyes, M. Graff, M. Montes-y Go´ mez, E. F. Morales, and J. Mart´ınez-Carranza. Term-weighting learning via genetic programming for text classification. Knowledge-Based Systems, 83:176-189, 2015.
[8] F.-A. Fortin, D. Rainville, M.-A. G. Gardner, M. Parizeau, C. Gagne´, et al. DEAP: Evolutionary algorithms made easy. The Journal of Machine Learning Research, 13(1):2171-2175, 2012.
[9] E. Grefenstette and M. Sadrzadeh. Experimental support for a categorical compositional distributional model of meaning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1394-1404. Association for Computational Linguistics, 2011. [OpenAIRE]
[10] M. Iqbal, W. Browne, and M. Zhang. Reusing building blocks of extracted knowledge to solve complex, large-scale Boolean problems. Evolutionary Computation, IEEE Transactions on, 18(4):465-480, Aug 2014.
[11] M. Iyyer, J. L. Boyd-Graber, L. M. B. Claudino, R. Socher, and H. Daume´ III. A neural network for factoid question answering over paragraphs. In Empirical Methods in Natural Language Processing (EMNLP), pages 633-644, 2014. [OpenAIRE]
[12] W. Kintsch. Predication. Cognitive science, 25(2):173-202, 2001.
[13] J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge, MA, 1992.
[14] K. Krawiec, J. Swan, and U.-M. OReilly. Behavioral program synthesis: Insights and prospects. In Genetic Programming Theory and Practice XIII, pages 169-183. Springer, 2016.
[15] O. Levy and Y. Goldberg. Linguistic regularities in sparse and explicit word representations. In Proceedings of the Eighteenth Conference on Computational Language Learning, pages 171-180, 2014.
[16] T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. CoRR, abs/1301.3781, 2013.
[1] Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin. A neural probabilistic language model. Journal of machine learning research, 3(Feb):1137-1155, 2003.
[2] U. Bhowan, M. Johnston, M. Zhang, and X. Yao. Evolving diverse ensembles using genetic programming for classification with unbalanced data. IEEE Transactions on Evolutionary Computation, 17(3):368-386, 2013.
[3] W. Blacoe and M. Lapata. A comparison of vector-based representations for semantic composition. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 546-556. Association for Computational Linguistics, 2012.
[4] P. A. Bosman and E. D. De Jong. Learning probabilistic tree grammars for genetic programming. In International Conference on Parallel Problem Solving from Nature, pages 192-201. Springer, 2004.
[6] R. Cummins and C. O'Riordan. An analysis of the solution space for genetically programmed term-weighting schemes in information retrieval. In P. S. P. M. D. Bell, editor, 17th Artificial Intelligence and Cognitive Science Conference (AICS 2006), Queen's University, Belfast, 2006.
[7] H. J. Escalante, M. A. Garc´ıa-Limo´ n, A. Morales-Reyes, M. Graff, M. Montes-y Go´ mez, E. F. Morales, and J. Mart´ınez-Carranza. Term-weighting learning via genetic programming for text classification. Knowledge-Based Systems, 83:176-189, 2015.
[8] F.-A. Fortin, D. Rainville, M.-A. G. Gardner, M. Parizeau, C. Gagne´, et al. DEAP: Evolutionary algorithms made easy. The Journal of Machine Learning Research, 13(1):2171-2175, 2012.
[9] E. Grefenstette and M. Sadrzadeh. Experimental support for a categorical compositional distributional model of meaning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1394-1404. Association for Computational Linguistics, 2011. [OpenAIRE]
[10] M. Iqbal, W. Browne, and M. Zhang. Reusing building blocks of extracted knowledge to solve complex, large-scale Boolean problems. Evolutionary Computation, IEEE Transactions on, 18(4):465-480, Aug 2014.
[11] M. Iyyer, J. L. Boyd-Graber, L. M. B. Claudino, R. Socher, and H. Daume´ III. A neural network for factoid question answering over paragraphs. In Empirical Methods in Natural Language Processing (EMNLP), pages 633-644, 2014. [OpenAIRE]
[12] W. Kintsch. Predication. Cognitive science, 25(2):173-202, 2001.
[13] J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge, MA, 1992.
[14] K. Krawiec, J. Swan, and U.-M. OReilly. Behavioral program synthesis: Insights and prospects. In Genetic Programming Theory and Practice XIII, pages 169-183. Springer, 2016.
[15] O. Levy and Y. Goldberg. Linguistic regularities in sparse and explicit word representations. In Proceedings of the Eighteenth Conference on Computational Language Learning, pages 171-180, 2014.
[16] T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. CoRR, abs/1301.3781, 2013.
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