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[1]刘澜. 基于词向量的中文分词方法研究 [D]. 哈尔滨工程大学, 2017.
[2]郭一鸣. 统计语言模型N-best重排序算法的研究 [D]. 哈尔滨工业大学, 2013.
[3]MAULDIN M L. Semantic rule based text generation [C]//10th International Conference on Computational Linguistics and 22nd Annual Meeting of the Association for Computational Linguistics. 1984: 376-380.
[4]CONROY J M, O'LEARY D P. Text summarization via hidden markov models [C]//Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval. 2001: 406-407.
[5]LAFFERTY J, MCCALLUM A, PEREIRA F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data [C]//Icml. 2001, 1(2): 3.
[6]SHANNON C E. A mathematical theory of communication [J]. The Bell system technical journal, 1948, 27(3): 379-423.
[7]HOPFIELD J J. Neural networks and physical systems with emergent collective computational abilities [J]. Proceedings of the national academy of sciences, 1982, 79(8): 2554-2558.
[8]HOCHREITER S, SCHMIDHUBER J. Long short-term memory [J]. Neural computation, 1997, 9(8): 1735-1780.
[9]CHUNG J, GULCEHRE C, CHO K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling [J]. arxiv preprint arxiv: 1412.3555, 2014.
[10]KALCHBRENNER N, ESPEHOLT L, SIMONYAN K, et al. Neural machine translation in linear time [J]. arxiv preprint arxiv: 1610.10099, 2016.
[11]GEHRING J, AULI M, GRANGIER D, et al. Convolutional sequence to sequence learning [C]//International conference on machine learning. PMLR, 2017: 1243-1252.
[12]VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [J]. Advances in neural information processing systems, 2017, 30.
[13]HAHN M. Theoretical limitations of self-attention in neural sequence models [J]. Transactions of the Association for Computational Linguistics, 2020, 8: 156-171.
[14]SANFORD C, HSU D J, TELGARSKY M. Representational strengths and limitations of transformers [J]. Advances in Neural Information Processing Systems, 2023, 36: 36677-36707.
[15]ACHIAM J, ADLER S, AGARWAL S, et al. Gpt-4 technical report [J]. arxiv preprint arxiv: 2303.08774, 2023.
[16]GRATTAFIORI A, DUBEY A, JAUHRI A, et al. The llama 3 herd of models [J]. arxiv preprint arxiv: 2407.21783, 2024.
[17]LIU C, WANG Y, FLANIGAN J, et al. Large language model unlearning via embedding-corrupted prompts [J]. Advances in Neural Information Processing Systems, 2024, 37: 118198-118266.
[18]LI J, TANG T, ZHAO W X, et al. Pre-trained language models for text generation: A survey [J]. ACM Computing Surveys, 2024, 56(9): 1-39.
[19]ZHANG D, Yu Y, Dong J, et al. Mm-llms: Recent advances in multimodal large language models [J]. arxiv preprint arxiv: 2401.13601, 2024.
[20]KUNG S,“Chinese Natural Language (Pre) processing: An Introduction”Towards Data Science,2020, Vol.20.
[21]李玲. 基于双词典机制的中文分词系统设计 [J]. 机械工程与自动化, 2013,(1): 17-19.
[22]罗宁, 徐俊刚, 郭洪韬. 基于Lucene的中文分词模块的设计和实现 [J]. 电子技术, 2012, 39(9): 54-56.
[23]姚茂建, 李晗静, 吕会华, 等. 基于BI_LSTM_CRF神经网络的序列标注中文分词方法 [J]. 现代电子技术, 2019, 42(1): 95-99.DOI: 10.16652/j.issn.1004-373x.2019.01.022.
[24]屈小刚. 汉语嵌入式TTS系统中的韵律建模和语音合成方法 [D]. 山东大学, 2006.
[25]张林. 基于web的定制产品用户评论情感分析系统 [D]. 大连理工大学, 2021.
[26]黄孝喜, 李晗雨, 王荣波, 等. 基于卷积神经网络与SVM分类器的隐喻识别 [J]. 数据分析与知识发现, 2018, 2(10): 77-83.
[27]耿向好. 基于历史信息的中文多层次句法分析研究 [D]. 苏州大学, 2008.
[28]LEEL H, RAO G, YU L C, et al. Overview of NLP-TEA 2016 shared task for Chinese grammatical error diagnosis [C]//Proceedings of the 3rd workshop on natural language processing techniques for educational applications (NLPTEA2016). 2016: 40-48.
[29]RAO G, YANG E, ZHANG B. Overview of NLPTEA-2020 shared task for Chinese grammatical error diagnosis [C]//Proceedings of the 6th workshop on natural language processing techniques for educational applications. 2020: 25-35.
[30]詹议. AI背景下NLP模型在高校网络育人研究中的应用 [J]. 科技创业月刊, 2023, 36(S1): 138-141.
[31]刘利, 史中琦, 崔希亮, 等. ChatGPT给国际中文教育带来的机遇与挑战——北京语言大学与美国中文教师学会联合论坛专家观点汇辑 [J]. 世界汉语教学, 2023, 37(3): 291-315.
[32]李海峰, 王炜. 人工智能支持下的智适应学习模式 [J]. 中国电化教育, 2018,(12): 88-95+112.
[33]江志杨. 大数据时代的数据安全处理技术分析 [J]. 智能物联技术, 2024, 56(4): 32-35.

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