This paper defined tourism environment, analyzed the impact of tourism development on the water environment, soil, vegetation and landscape and proposed main measures for protecting tourism environment on this basis.
It is shown that the supersymmetry breaking can be realized dynamically by Nambu-Jona-Lasinio(NJL)mechanism.The supersymmetry behavior at finite temperature is also investigated and shown that the supersymmetry broken...It is shown that the supersymmetry breaking can be realized dynamically by Nambu-Jona-Lasinio(NJL)mechanism.The supersymmetry behavior at finite temperature is also investigated and shown that the supersymmetry broken dynamically at zero temperature can be restored at finite temperature.展开更多
命名实体是电子病历中相关医学知识的主要载体,因此,临床命名实体识别(Clinical Named Entity Recognition,CNER)也就成为了临床文本分析处理的基础性任务之一.由于文本结构和语言等方面的特殊性,面向中文电子病历(Electronic Medical R...命名实体是电子病历中相关医学知识的主要载体,因此,临床命名实体识别(Clinical Named Entity Recognition,CNER)也就成为了临床文本分析处理的基础性任务之一.由于文本结构和语言等方面的特殊性,面向中文电子病历(Electronic Medical Records,EMRs)的临床命名实体识别依然存在着巨大的挑战.本文提出了一种基于多头自注意力神经网络的中文临床命名实体识别方法.该方法使用了一种新颖的融合领域词典的字符级特征表示方法,并在BiLSTM-CRF模型的基础上,结合多头自注意力机制来准确地捕获字符间潜在的依赖权重、语境和语义关联等多方面的特征,从而有效地提升了中文临床命名实体的识别能力.实验结果表明本文方法超过现有的其他方法获得了较优的识别性能.展开更多
文摘This paper defined tourism environment, analyzed the impact of tourism development on the water environment, soil, vegetation and landscape and proposed main measures for protecting tourism environment on this basis.
基金Supported in part by the National Natural Science Foundation of China。
文摘It is shown that the supersymmetry breaking can be realized dynamically by Nambu-Jona-Lasinio(NJL)mechanism.The supersymmetry behavior at finite temperature is also investigated and shown that the supersymmetry broken dynamically at zero temperature can be restored at finite temperature.
基金湖南省自然科学基金资助项目(2018JJ2534)网络犯罪侦查湖南省普通高校重点实验室开放基金资助项目(2020WLFZZC003)+3 种基金国家重点研发计划资助项目(2016YFC0901705)湖南省重大科技专项(2017SK1040)Natural Science Foundation of Hunan Province(2018JJ2534)高新技术产业科技创新引领计划(2020GK2029)
文摘命名实体是电子病历中相关医学知识的主要载体,因此,临床命名实体识别(Clinical Named Entity Recognition,CNER)也就成为了临床文本分析处理的基础性任务之一.由于文本结构和语言等方面的特殊性,面向中文电子病历(Electronic Medical Records,EMRs)的临床命名实体识别依然存在着巨大的挑战.本文提出了一种基于多头自注意力神经网络的中文临床命名实体识别方法.该方法使用了一种新颖的融合领域词典的字符级特征表示方法,并在BiLSTM-CRF模型的基础上,结合多头自注意力机制来准确地捕获字符间潜在的依赖权重、语境和语义关联等多方面的特征,从而有效地提升了中文临床命名实体的识别能力.实验结果表明本文方法超过现有的其他方法获得了较优的识别性能.
