[1] 杨锦锋, 于秋滨, 关毅,等. 电子病历命名实体识别和实体关系抽取研究综述[J]. 自动化学报, 2014, 40(8):1537-1562.
[2] UZUNER O, SOUTH B R, SHEN S, et al. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text[J]. Journal of the American medical informatics Association jamia, 2011, 18(5):552-556.
[3] SUN W, RUMSHISKY A, UZUNER O. Evaluating temporal relations in clinical text:2012 i2b2 challenge[J]. Journal of the American medical informatics Association jamia, 2013, 20(5):806-813.
[4] PRADHAN S, ELHADAD N, CHAPMAN W, et al. SemEval-2014 task 7:analysis of clinical text[C]//Conference:Proceedings of the 8th International workshop on semantic evaluation. SemEval 2014, Dublin, Ireland, 2014:54-62.
[5] BETHARD S, DERCZYNSKI L, SAVOVA G, et al. SemEval-2015 task 6:clinical tempeval[C]//Conference:proceedings of the 9th International workshop on semantic evaluation. SemEval 2015, Denver, Colorado, 2015:806-814.
[6] BETHARD S, SAVOVA G, CHEN W T, et al. SemEval-2016 Task 12:clinical tempeval[C]//Conference:Proceedings of the 10th International workshop on semantic evaluation. San Diego, California, 2016:1052-1062.
[7] CCKS2017-全国知识图谱与语义计算大会[EB/OL].[2017-08-26]. http://www.ccks2017.com/.
[8] 王云吉. 基于层叠条件随机场的电子病历命名实体识别[D]. 吉林:吉林大学, 2014.
[9] 汤步洲, 王晓龙, 王轩. 置信度加权在线序列标注算法[J]. 自动化学报, 2011, 37(2):188-195.
[10] TSURUOKA Y, TSUJⅡ J. Boosting precision and recall of dictionary-based protein name recognition[C]//ACL workshop on natural language processing in biomedicine. Association for computational linguistics, Sapporo, 2003:41-48.
[11] ALFRED R, LEONG L C, ON C K, et al. Malay named entity recognition based on rule-based approach[J]. International journal of machine learning & computing, 2014, 4(3):300-306.
[12] LAWRENCE R. RABINER. A tutorial on hidden markov models and selected applications in speech recognition[J]. Readings in speech recognition, 1990, 77(2):267-296.
[13] BERGER A L, PIETRA V J D, PIETRA S A D. A maximum entropy approach to natural language processing[J]. Computational linguistics, 1996, 22(1):39-71.
[14] LAFFERTY J D, MCCALLUM A, PEREIRA F C N. Conditional random fields:probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the Eighteenth International Conference on Machine Learning (ICML-2001). Williamstown:Morgan Kauf-mann, 2001:282-289.
[15] GRAVES A, SCHMIDHUBER J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J]. Neural networks the official journal of the international neural network society, 2005, 18(5-6):602.
[16] HUANG Z, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging[J]. Computer science, 2015, 20(2):508-517.
[17] JIANGLU H, XUE S, ZENGJIAN L, et al. HITSZ_CNER:a hybrid system for entity recognition from chinese clinical text[C]//Proceedings of the Evaluation Task at the China Conference on Knowledge Graph and Semantic Computing (CCKS 2017). China, 2017:25-30.
[18] JINHANG W, XIAO H, RONGSHENG Z, et al. Clinical named entity recognition via bi-directional LSTM-CRF model[C]//Proceedings of the Evaluation Task at the China Conference on Knowledge Graph and Semantic Computing (CCKS 2017). China, 2017:31-36.
[19] OUYANG E, LI Y X, JIN L, et al. Exploring n-gram character presentation in bidirectional RNN-CRF for chinese clinical named entity recognition[C]//Proceedings of the Evaluation Task at the China Conference on knowledge graph and semantic computing (CCKS 2017). China, 2017:37-42.
[20] XIA Y H, WANG Q. Clinical named entity recognition:ECUST in the CCKS-2017 shared task 2[C]//Proceedings of the evaluation task at the China conference on knowledge graph and semantic computing (CCKS 2017). China, 2017:43-48.
[21] CHEN Y X, ZHANG G, FANG H Z, et al. Clinical named entity recognition method based on CRF[C]//Proceedings of the evaluation task at the China conference on knowledge graph and semantic computing (CCKS 2017). China, 2017:49-54.
[22] LI Z Z, ZHANG Q, LIU Y, FENG D W, et al. Recurrent neural networks with specialized word embedding for chinese clinical named entity recognition[C]//Proceedings of the evaluation task at the China conference on knowledge graph and semantic computing (CCKS 2017). Evaluation tasks at CCKS 2017, China, 2017:55-60.
[23] GENG D W. Clinical name entity recognition using conditional random field with augmented features[C]//Proceedings of the evaluation task at the China conference on knowledge graph and semantic computing (CCKS 2017). China, 2017:61-68.
[24] 章成志, 苏新宁. 基于条件随机场的自动标引模型研究[J]. 中国图书馆学报, 2008, 34(5):89-94.
[25] 石崇德, 王惠临. 统计机器翻译中文分词优化技术研究[J]. 现代图书情报技术, 2012, 28(4):29-34.
[26] 李月伦, 常宝宝. 基于最大间隔马尔可夫网模型的汉语分词方法[J]. 中文信息学报, 2010, 24(1):8-14.
[27] 王昊, 邓三鸿, 苏新宁. 基于字序列标注的中文关键词抽取研究[J]. 现代图书情报技术, 2011, 27(12):39-45.
[28] 燕杨, 文敦伟, 王云吉,等. 基于层叠条件随机场的中文病历命名实体识别[J]. 吉林大学学报(工), 2014, 44(6):1843-1848.
[29] 张海楠, 伍大勇, 刘悦,等. 基于深度神经网络的中文命名实体识别[J]. 中文信息学报, 2017, 31(4):28-35.
[30] 来斯惟. 基于神经网络的词和文档语义向量表示方法研究[D]. 北京:中国科学院自动化研究所, 2016.
[31] 计峰. 自然语言处理中序列标注模型的研究[D]. 上海:复旦大学, 2012.
[32] ZHENG S, WANG F, BAO H, et al. Joint extraction of entities and relations based on a novel tagging scheme[C]//The 55th annual meeting of the association for computational linguistics (ACL). Association for Computational Linguistics. Vancouver, Canada, July 30-August 4, 2017:1227-1236.
[33] GU Q, LI Z, HAN J. Joint feature selection and subspace learning[C]//Conference:IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, AAAI press, Barcelona, Catalonia, Spain, 2011:1294-1299.
[34] 柯彼德. 试论汉语语素的分类[J]. 世界汉语教学, 1992(1):1-12.
[35] 夏天. 词语位置加权TextRank的关键词抽取研究[J]. 现代图书情报技术, 2013, 29(9):30-34.
[36] 唐晓波, 肖璐. 基于依存句法分析的微博主题挖掘模型研究[J]. 情报科学, 2015(9):61-65.
[37] 章成敏, 许鑫, 章成志. 条件随机场标引模型的性能影响因素分析[J]. 现代图书情报技术, 2008(6):34-40.
[38] 陈锋, 翟羽佳, 王芳. 基于条件随机场的学术期刊中理论的自动识别方法[J]. 图书情报工作, 2016,60(2):122-128.
[39] 周志华. 机器学习:=Machine learning[M]. 北京:清华大学出版社, 2016. 作者贡献说明:孙安:提出研究思路,制定实验方案,撰写论文初稿; 于英香:设计论文框架,提出修改建议; 罗永刚:为研究选题提供素材和指导; 王祺:提供技术指导。 |