Communications of the Korean Institute of Information Scientists and Engineers (정보과학회지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

의미역 인식에 적용된 심층학습 기법 동향

  • Published : 2015.10.18
Download PDF
⟨ Previous Next ⟩

Abstract

Keywords

References

  1. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, "Natural language processing(almost) from scratch", The Journal of Machine Learning Research, 12, pp. 2493-2537, 2011.
  2. M. Surdeanu et aI., "The CoNLL-2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies," Proc. of the CoNLL-2008, pp.159-177, 2008
  3. Veronis, Jean, and Nancy M. Ide. "Word sense disambiguation with very large neural networks extracted from machine readable dictionaries." Proceedings of the 13th conference on Computational linguistics-Volume 2. Association for Computational Linguistics, 1990.
  4. 조정미, 김길창, "한국어 의미 해석시 중의성 해소에 대한 연구,", 정보과학회지, 제24권 제7호, pp.71-83, 1996.
  5. Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin, "A neural probabilistic language model", The Journal of Machine Learning Research, 3, pp.1137-1155, 2003.
  6. Holger Schwenk and Jean-Luc Gauvain. "Training New-al Network Language Models On Very Large Corpora," in Proc. Joint Conference HLT/EMNLP, October 2005.
  7. Collobelt, Ronan, and Jason Weston. "Fast semantic extraction using a novel neural network architecture." Annual meeting-association for computational linguistics. Vol. 45. No. 1. 2007.
  8. R. Collobert and J. Weston, "A unified architecture for natural language processing: Deep neural networks with multitask learning," in International Conference on Machine Learning, ICML, 2008.
  9. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-ased learning applied to document recognition", Proceedings ofthe IEEE, 86(11), pp. 2278-2324, 1998. https://doi.org/10.1109/5.726791
  10. Bridle, John S. "Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition." Neurocomputing. Springer Berlin Heidelberg, pp. 227-236, 1990.
  11. Fonseca, Erick R., and Joao Luis G. Rosa. "A two-step convolutional neural network approach for semantic role labeling." Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013.
  12. P. D. Turney and P. Pantel, "From Frequency to Meaning: Vector Space Models of Semantics," Journal of Artificial Intelligence Research, vol. 37, pp. 141 - 188, 2010. https://doi.org/10.1613/jair.2934
  13. Schuster, Mike, and Kuldip K. Paliwal. "Bidirectional recurrent neural networks." Signal Processing, IEEE Transactions on 45.11, pp. 2673-2681, 1997. https://doi.org/10.1109/78.650093
  14. Kombrink, Stefan, et al. "Recurrent Neural Network Based Language Modeling in Meeting Recognition." INTERSPEECH. 2011.
  15. S. Hochreiter and J. Schmidhuber, "Long short-term memory", Neural computation,9(8), pp. 1735-1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735
  16. Zhou, Jie, and Wei Xu. "End-to-end Learning of Semantic Role Labeling Using Recurrent Neural Networks.", In Proc. 53rd ACL, pp.1127-1137, 2015.
  17. Graves, Alex, et al. "A novel connectionist system for unconstrained handwriting recognition." Pattern Analysis and Machine Intelligence, IEEE Transactions on 31.5, pp. 855-868, 2009. https://doi.org/10.1109/TPAMI.2008.137
  18. Wang, Zhen, et al. "Chinese Semantic Role Labeling with Bidirectional Recurrent Neural Networks.", In Proc. of the 2015 Conference on Empirical Methods in Natural Language Processing(EMNLP), pp. 1626-1631, 2015.
  19. FitzGerald, Nicholas, et al. "Semantic Role Labeling with Neural Network Factors." In Proc. of the 2015 Conference on Empirical Methods in Natural Language Processing(EMNLP), pp. 960-970, 2015.