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苏劲松
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教授
- 入职时间：2011-07-25
- 所在单位：信息学院
- 学历：博士研究生毕业
- 性别：男
- 学位：工学博士学位
- 在职信息：在职
访问量：
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[201]苏劲松,Bilingual correspondence recursive autoencoders for statistical machine translation.Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing,1248-1258.
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[202]苏劲松,Shallow convolutional neural network for implicit discourse relation recognition.Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing,2230-2235.
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[203]苏劲松,A context-Aware topic model for statistical machine translation.ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference,1229-238.
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[204]史晓东,苏劲松.Unsupervised word sense induction using rival penalized competitive learning.Engineering Applications of Artificial Intelligence,2015,41166-174.
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[205]苏劲松.Regularized structured perceptron: A case study on chineseword segmentation, POS tagging and parsing.14th Conference of the European Chapter of the Association for Computational Linguistics 2014, EACL 2014,164-173.
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[206]苏劲松,吴清强,Improved statistical machine translation model with topic-based paraphrase.Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science),2014,48(10):1843-1849.
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[207]苏劲松,An improved maximal entropy based bracketing transduction grammar translation model with ensemble learning.Journal of Computational Information Systems,10(4):1669-1676.
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[208]苏劲松,An approach to n-gram language model evaluation in phrase-based statistical machine translation.Proceedings - 2012 International Conference on Asian Language Processing, IALP 2012,201-204.
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[209]苏劲松,Translation model adaptation for statistical machine translation with monolingual topic information.50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference,1459-468.
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[210]苏劲松.Naive Bayes classification algorithm based on optimized training data.Advanced Materials Research,460-464.
