과제정보
연구 과제번호 : (엑소브레인-1세부) 휴먼 지식증강 서비스를 위한 지능진화형 WiseQA 플랫폼 기술 개발
연구 과제 주관 기관 : 정보통신기술진흥센터
참고문헌
- D. Hays, Dependency theory: a formalism and some observations, Language, pp. 511-525, 1964.
- O. Vinyals, M. Fortunato and N. Jaitly, Pointer Networks, Advances in Neural Information Processing Systems, pp. 2674-2682, 2015.
- D. Bahdanau, K. Cho and Y. Bengio, Neural machine translation by jointly learning to align and translate, arXiv preprint arXiv:1409.0473, 2014.
- J. Li, E. Lee and J.H. Lee, Sequence-to-sequence based Morphological Analysis and Part-Of-Speech Tagging for Korean Language with Convolutional Features, Journal of KIISE, Vol. 44, No. 1, pp. 57-62, 2017. (in Korean) https://doi.org/10.5626/JOK.2017.44.1.57
- C. Lee, Named Entity Recognition using Long Short-Term Memory Based Recurrent Neural Network, Proc. of the KIISE Korea Computer Congress 2015, pp. 645-647, 2015. (in Korean)
- C. Park, K.H. Choi, C. Lee and S. Lim, Korean Coreference Resolution with Guided Mention Pair Model using Deep Learning, ETRI Journal, Vol. 38, No. 6, pp. 1207-1217, 2016. (in Korean) https://doi.org/10.4218/etrij.16.0115.0896
- J. Bae and C. Lee, Korean Semantic Role Labeling using Stacked Bidirectional LSTM-CRFs, Journal of KIISE, Vol. 44, No. 1, pp. 36-43, 2017. (in Korean) https://doi.org/10.5626/JOK.2017.44.1.36
- Y. Wu, M. Schuster, Z. Chen, Q.V. Le and M. Norouzi, Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, arXiv preprint arXiv:1609. 08144, 2016.
- K. Choi and C. Lee, End-to-end Document Summarization using Copy Mechanism and Input Feeding, Proc. of the 28th Annual Conference on Human & Cognitive Language Technology, pp. 56-61, 2016. (in Korean)
- C. Lee, J. Kim and J. Kim, Korean Dependency Parsing using Deep Learning, Proc. of the 26th Annual Conference on Human & Cognitive Language Technology, pp. 87-91, 2014. (in Korean)
- J. Li and J.H. Lee, Korean Transition-based Dependency Parsing with Recurrent Neural Network, KIISE Transactions on Computing Practices, Vol. 21, No. 8, pp. 567-571, 2015. (in Korean) https://doi.org/10.5626/KTCP.2015.21.8.567
- S.H. Na, Phrase-Based Dependency Parsing Using String-to-Dependency SMT, Proc. of the KIISE Korea Computer Congress 2015, pp. 657-659, 2015. (in Korean)
- S.H. Na, K. Kim and Y.K. Kim, Stack LSTMs for Transition-Based Korean Dependency Parsing, Proc. of the KIISE Korea Computer Congress 2016, pp. 732-734, 2016. (in Korean)
- S.H. Na, J.H. Shin and K. Kim, Improving Stack LSTMs by Combining Syllables and Morphemes for Korean Dependency Parsing, Proc. of the 28th Annual Conference on Human & Cognitive Language Technology, pp. 9-13, 2016.
- J. Nivre, Non-Projective Dependency Parsing in Expected Linear Time, Proc. of the ACL-IJCNLP, pp. 351-359, 2009.
- R. McDonald, K. Crammer and F. Pereira, Online Large-margin Training of Dependency Parsers, Proc. of the ACL, pp. 91-98, 2005.
- K. Cho, B. Van Merrienboer and C. Gulcehre, Learning phrase representation using RNN encoder-decoder for statistical machine translation, arXiv preprint arXiv:1406.1078, 2014.
- K. Yao, B. Peng, Y. Zhang, D. Yu, G. Zweig, and Y. Shi, Spoken Language Understanding Using Longe Short-Term Memory Neural Networks, Spoken Language Technology Workshop (SLT), 2014 IEEE, pp. 189-194, 2014.
- J. Chung, C. Gulcehre, K.H. Cho, and Y. Bengio, Empirical Evaluation of Gated Recurrent Networks on Sequence Modeling, arXiv preprint arXiv:1412. 3555, 2014.
- The National Institute of the Korean Language, The 21 century Sejong plan. 2012. (in Korean)
- S. Lim, Y.T. Kim and D.Y. Ra, Korean Dependency Parsing Based on Machine Learning of Feature Weights, Journal of KIISE: Software and Applications, Vol. 38, No. 4, pp. 214-223, 2011. (in Korean)
- S.H. Na, J. Li, J.H. Shin and K. Kim, Stack LSTMs with Recurrent Controllers for Korean Dependency Parsing, Proc. of the KIISE 2016 winter conference, pp. 446-448, 2016. (in Korean)