@article{_blstm_2020,
	title = {{基于BLSTM网络的医学时间短语识别}},
	volume = {37},
	issn = {1001-3695},
	url = {https://kns.cnki.net/KCMS/detail/51.1196.TP.20190124.0848.001.html},
	abstract = {从医学文本中识别时间短语是临床医学自然语言处理的关键技术之一。传统基于规则和机器学习的方法,需要设计复杂规则和提取特征,而且大多数系统采用串行方法会导致错误的传播。提出基于双向长短时记忆网络(BLSTM)的神经网络架构,在识别时间表示式的同时判别它们的类型:首先使用卷积神经网络(CNN)学习得到单词的字符级别向量和大规模生物医学背景语料上训练得到的词向量进行组合作为BLSTM的输入,然后使用BLSTM网络学习单词的上下文语义表示,最后使用条件随机场(CRF)对BLSTM输出的序列进行标签优化。实验基于SemEval-2016任务12,结果表明没有添加任何特征的神经网络学习方法比该任务中官方提供...},
	language = {中文},
	number = {4},
	urldate = {2020-04-07},
	journal = {计算机应用研究},
	author = {张, 顺利 and 王, 应军 and 姬, 东鸿},
	year = {2020},
	pages = {1--5},
}

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