想, 为自然语言处理提供了一种将特征表示和应用实现独立分开的可行方法,这将使得在领域任务和语言之间的泛化迁移变得较为容易.
最后我觉得深度学习在自然语言处理领域主要存在这样几个问题需要考虑，首先由于每个汉字都包含不同的含义， 需要为每个含义获取相应的表示. 另外使用同音词或者多义词来为词语学习单一表征反而可能会影响最终的表征结果，由于多个含义之间的相互影响，不能准确表示任何一个含义.其次，需要进一步考虑 训练语料问题，如何保证系统的鲁棒性、通用性，保证能够在不同领域都得到较好的效果，另外需要考虑新生词、网络用语等的识别问题.最后，需要考虑语料是否是越多越好，在训练学习的过程中，需要能够检测训练情况，避免过大的数据训练，破坏汉字的分布式表示.
参考文献
[1] Hinton G E,Salakhutdinov R R.Reducing the dimensionality of data with neural networks.Science,2006,313(5786):504-507.
[2]Bengio Y,Ducharme R,Vincent P,et al.A Neural Probabilistic Language Mode[J].J.Mach.Learn.Res.,2003,3:1137-1155.
[3]Bordes A,Weston J,Collobert R,et al.Learning structured embeddings of
knowledge-bases[C]//Conference on Artificial Intelligence.San Francisco,California:AAAI,2011. [4]Mikolov T,Chen K,Corrado G,et al.Efficient estimation of word representations in vector space[J].arXiv preprint arXiv:1301.3781,2013.
[5]Kim Y.Convolutional neural networks for sentence classification[J].arXiv preprint arXiv:1408.5882,2014.
[6]Sutskever I,Vinyals O,Le Q V.Sequence to Sequence Learning with Neural
Networks[C]//.Ghahramani Z,Welling M,Cortes C,et al.Advances in Neural Information Processing Systems 27.Montreal,Canada:Curran Associates,Inc,2014:3104-3112.
[7]Tai K S,Socher R,Manning C D.Improved semantic representations from treestructured long short-term memory networks[J].arXiv:1503.00075,2015. [8]Mikolov T, Deoras A, Povey D, Burget L,Cernock′y J H.Strategies for training large scale neural network languagemodels. In: Proceedings of the 2011 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU).Waikoloa, Hawaii, USA: IEEE Press, 2011. 196?201.
[9]Socher R, Huang E H, Pennington J, Ng A Y, Manning C D.Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Proceedings of the 2011 Advancesin Neural Information Processing Systems 24 (NIPS 11).Granada, Spain: MIT Press, 2011. 801?809. [10]Socher R, Huval B, Manning C D, Ng A Y. Semantic compositionality through recursive matrixvector spaces. In: Proceedings of the 2012 Joint Conference on Empirical Methodsin
Natural Language Processing and Computational Natural Language Learning. Jeju Island, Korea: ACL Press, 2012.1201?1211.
[11]Socher R, Perelygin A, Wu J Y, Chuang J, Manning C D,Ng A Y, Potts C. Recursive
deepmodels for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013).Seattle, USA: ACL Press,2013. 1631?1642.
[12]Pinheiro P O, Collobert R. Recurrent convolutional neural networks for scene labeling. In: Proceedings of the 31st International Conference on Machine Learning (ICML 14).Beijing, China, 2014. 82?90.
[13]Le Q V. Building high-level features using large scale unsupervised learning. In: Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.Vancouver, BC: IEEE, 2013. 8595?8598.
[14]田渊栋. 阿法狗围棋系统的简要分析.
, 2016, 42(5):671?675.
自动化学报