References
- M. Sundermeyer, and R. Schluter, "LSTM Neural Networks for Language Modeling" Interspeech, pp. 194-197, Sep. 2012.
- mykang, bgkim and jslee, "Word Sense Disambiguation using Word2Vec", Proceeding of 27th Conference on Human & Cognitive Language Technology, pp. 81-84, Oct. 2015
- jcshin, and, cyock, "Homograph Word Sense Disambiguation using Korean Lexical Semantic Map(UWordMap) and Word-Embedding", Proceeding of Korean Computer Congress 2016, pp. 702-704, Jun. 2016
- D. Yuan, J. Richardson, R. Doherty, C. Evans and E. Altendorf, "Word Sense Disambiguation with Neural Language Models", arXivpreprint arXiv:1603.07012, 2016.
- jhmin, jwjeon, khsong, and yskim, "Study on Word Sense Disambiguation Using Recurrent Neural Network for Korean", Proceeding of Winter Conference on Korean Association of Computer Education 2017, pp. 93-96, Jan. 2017.
- jsbae, and cklee, "End-to-end Learning of Korean Semantic Role Labeling Using Bidirectional LSTM CRF", Proceeding of 42th Winter Conference on Korean Institute of Information Scientists and Engineers, pp. 566-568, Dec. 2015.
- C. Irsoy, and C. Cardie, "Opinion Mining with Deep Recurrent Neural Networks", Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing, pp. 720-728, 2014.
- H. Sak, A. Senior, and F. Beaufays, " Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition", arXiv preprint arXiv:1402.1128, 2014.
- Junyoung Chung, C. Gulcehre, KyungHyun Cho, and Y. Bengio, "Empirical evaluation of gated recurrent neural networks on sequence modeling", arXiv preprint arXiv:1412.3555, 2014.
- shjung, "Inference of Context-Free Grammars using Binary Third-order Recurrent Neural Networks with Genetic Algorithms", Journal of The Korea Society of Computer and Information, Vol. 17, No. 3, pp. 11-25, Mar. 2012 https://doi.org/10.9708/jksci.2012.17.3.011
- hmkim, jmyoon, jhan, kmbae, and yjko, "Syllable-based Korean POS Tagging using POS Distribution and Bidirectional LSTM CRFs", Proceeding of 28th Conference on Human & Cognitive Language Technology, pp. 3-8, Oct. 2016
- smhan, "Deep Learning Architectures and Applications", Journal of Intelligence Information System, Vol. 22, No. 2, pp. 127-142, Jun. 2016 https://doi.org/10.13088/jiis.2016.22.2.127
- Y. Bengio, P. Simard, and P. Frasconi, "Learning Long-Term Dependencies with Gradient Descent is Difficult", IEEE Transactions on Neural Networks, Vol. 5, No. 2, Mar. 1994
- bmkang, "Text Context and Word Meaning: Latent Semantic Analysis", Journal of the Linguistic Society of Korea, Vol. 68, pp. 3-34, Apr. 2014.
- Gensim, https://radimrehurek.com/gensim/models/word2vec.html
- T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word representations in vector space". arXivpreprint arXiv: 1301.3781. 2013.
- mbchung, "Color matching application which can help color blind people based on smart phone", Journal of The Korea Society of Computer and Information, Vol.20, No. 5, pp. 65-72, May. 2015 https://doi.org/10.9708/jksci.2015.20.5.065
- National Institute of Korean Language, http://ithub.korean.go.kr,
- hgkim, mbkang, and jhhong, "21st Century Sejong Modern Korean Corpora: Results and Expectations", Proceeding of 19th Conference on Human & Cognitive Language Technology, pp. 311-316, Oct. 2007
- shchoi, jsseol and sglee, "On Word Embedding Models and Parameters Optimized for Korean", Proceeding of 28th Conference on Human & Cognitive Language Technology, pp. 252-256, Oct. 2016
- Korean Wikipedia, https://ko.wikipedia.org/
- Namuwiki, https://namu.wiki/
- SENSEAVAL-2, http://www.hipposmond.com/senseval2/
- Keras, http://keras.io
Cited by
- 양방향 LSTM을 적용한 단어의미 중의성 해소 감정분석 vol.5, pp.1, 2020, https://doi.org/10.36498/kbigdt.2020.5.1.197
- Improving the Performance of Vietnamese-Korean Neural Machine Translation with Contextual Embedding vol.11, pp.23, 2017, https://doi.org/10.3390/app112311119