Objective To verify the efficacy and safety of conversion from cyclosporine ( CsA) to tacrolimus ( Tac) in renal transplant recipients. Methods The clinical data of conversion from CsA to Tac in renal transplant recip...Objective To verify the efficacy and safety of conversion from cyclosporine ( CsA) to tacrolimus ( Tac) in renal transplant recipients. Methods The clinical data of conversion from CsA to Tac in renal transplant recipients were retrospectively analyzed. In 97 petients undergoing kidney transplantation,there were 62 cases of chronic al-展开更多
航行通告是民用航空情报领域的重要情报资料,针对中文航行通告专业名词较多、格式不统一及语义复杂等问题,提出了一种基于BERT-Bi-LSTM-CRF的实体识别模型,对航行通告E项内容中事件要素实体进行抽取。首先通过BERT(bidirectional encode...航行通告是民用航空情报领域的重要情报资料,针对中文航行通告专业名词较多、格式不统一及语义复杂等问题,提出了一种基于BERT-Bi-LSTM-CRF的实体识别模型,对航行通告E项内容中事件要素实体进行抽取。首先通过BERT(bidirectional encoder representations from transforms)模型对处理后的向量进行预训练,捕捉丰富的语义特征,然后传送至双向长短期记忆网络(bidirectional long short-term memory,Bi-LSTM)模型对上下文特征进行提取,最后利用条件随机场(conditional random field,CRF)模型对最佳实体标签预测并输出。收集并整理机场类航行通告相关的原始语料,经过文本标注与数据预处理,形成了可用于实体识别实验的训练集、验证集和评价集数据。基于此数据与不同的实体识别模型进行对比实验,BERT-Bi-LSTM-CRF模型的准确率为89.68%、召回率为81.77%、F_(1)为85.54%,其中F 1相比现有模型得到有效提升,结果验证了该模型在机场类航行通告中要素实体识别的有效性。展开更多
Background:The medical records of traditional Chinese medicine(TCM)contain numerous synonymous terms with different descriptions,which is not conducive to computer-aided data mining of TCM.However,there is a lack of m...Background:The medical records of traditional Chinese medicine(TCM)contain numerous synonymous terms with different descriptions,which is not conducive to computer-aided data mining of TCM.However,there is a lack of models available to normalize synonymous TCM terms.Therefore,construction of a synonymous term conversion(STC)model for normalizing synonymous TCM terms is necessary.Methods:Based on the neural networks of bidirectional encoder representations from transformers(BERT),four types of TCM STC models were designed:Models based on BERT and text classification,text sequence generation,named entity recognition,and text matching.The superior STC model was selected on the basis of its performance in converting synonymous terms.Moreover,three misjudgment inspection methods for the conversion results of the STC model based on inconsistency were proposed to find incorrect term conversion:Neuron random deactivation,output comparison of multiple isomorphic models,and output comparison of multiple heterogeneous models(OCMH).Results:The classification-based STC model outperformed the other STC task models.It achieved F1 scores of 0.91,0.91,and 0.83 for performing symptoms,patterns,and treatments STC tasks,respectively.The OCMH method showed the best performance in misjudgment inspection,with wrong detection rates of 0.80,0.84,and 0.90 in the term conversion results for symptoms,patterns,and treatments,respectively.Conclusion:The TCM STC model based on classification achieved superior performance in converting synonymous terms for symptoms,patterns,and treatments.The misjudgment inspection method based on OCMH showed superior performance in identifying incorrect outputs.展开更多
“退耕还林”的本质是把一度为了生产粮食而毁林开垦的土地,停止耕种,重新造林,恢复森林。目前不少译文把“退耕还林”译为grain for green(绿化),或returning farmland toforestland(把农地转为林地)等,使人易误认为是荒山造林绿化(荒...“退耕还林”的本质是把一度为了生产粮食而毁林开垦的土地,停止耕种,重新造林,恢复森林。目前不少译文把“退耕还林”译为grain for green(绿化),或returning farmland toforestland(把农地转为林地)等,使人易误认为是荒山造林绿化(荒山→森林)或把现有农业用地转变为林业用地(农地→林地)。文章认为,要把“退耕还林”译得科学规范,必须抓住其本质。通过分析比较,给出了conversion of farmland back to forests,transfer of farmland back toforests和forest rehabilitation from slope agriculture等几种参考译法。展开更多
文摘Objective To verify the efficacy and safety of conversion from cyclosporine ( CsA) to tacrolimus ( Tac) in renal transplant recipients. Methods The clinical data of conversion from CsA to Tac in renal transplant recipients were retrospectively analyzed. In 97 petients undergoing kidney transplantation,there were 62 cases of chronic al-
文摘航行通告是民用航空情报领域的重要情报资料,针对中文航行通告专业名词较多、格式不统一及语义复杂等问题,提出了一种基于BERT-Bi-LSTM-CRF的实体识别模型,对航行通告E项内容中事件要素实体进行抽取。首先通过BERT(bidirectional encoder representations from transforms)模型对处理后的向量进行预训练,捕捉丰富的语义特征,然后传送至双向长短期记忆网络(bidirectional long short-term memory,Bi-LSTM)模型对上下文特征进行提取,最后利用条件随机场(conditional random field,CRF)模型对最佳实体标签预测并输出。收集并整理机场类航行通告相关的原始语料,经过文本标注与数据预处理,形成了可用于实体识别实验的训练集、验证集和评价集数据。基于此数据与不同的实体识别模型进行对比实验,BERT-Bi-LSTM-CRF模型的准确率为89.68%、召回率为81.77%、F_(1)为85.54%,其中F 1相比现有模型得到有效提升,结果验证了该模型在机场类航行通告中要素实体识别的有效性。
基金The National Key R&D Program of China supported this study(2017YFC1700303).
文摘Background:The medical records of traditional Chinese medicine(TCM)contain numerous synonymous terms with different descriptions,which is not conducive to computer-aided data mining of TCM.However,there is a lack of models available to normalize synonymous TCM terms.Therefore,construction of a synonymous term conversion(STC)model for normalizing synonymous TCM terms is necessary.Methods:Based on the neural networks of bidirectional encoder representations from transformers(BERT),four types of TCM STC models were designed:Models based on BERT and text classification,text sequence generation,named entity recognition,and text matching.The superior STC model was selected on the basis of its performance in converting synonymous terms.Moreover,three misjudgment inspection methods for the conversion results of the STC model based on inconsistency were proposed to find incorrect term conversion:Neuron random deactivation,output comparison of multiple isomorphic models,and output comparison of multiple heterogeneous models(OCMH).Results:The classification-based STC model outperformed the other STC task models.It achieved F1 scores of 0.91,0.91,and 0.83 for performing symptoms,patterns,and treatments STC tasks,respectively.The OCMH method showed the best performance in misjudgment inspection,with wrong detection rates of 0.80,0.84,and 0.90 in the term conversion results for symptoms,patterns,and treatments,respectively.Conclusion:The TCM STC model based on classification achieved superior performance in converting synonymous terms for symptoms,patterns,and treatments.The misjudgment inspection method based on OCMH showed superior performance in identifying incorrect outputs.
文摘“退耕还林”的本质是把一度为了生产粮食而毁林开垦的土地,停止耕种,重新造林,恢复森林。目前不少译文把“退耕还林”译为grain for green(绿化),或returning farmland toforestland(把农地转为林地)等,使人易误认为是荒山造林绿化(荒山→森林)或把现有农业用地转变为林业用地(农地→林地)。文章认为,要把“退耕还林”译得科学规范,必须抓住其本质。通过分析比较,给出了conversion of farmland back to forests,transfer of farmland back toforests和forest rehabilitation from slope agriculture等几种参考译法。
