针对传统情感分析任务中,使用Word2Vec(word to vector)模型生成文本词向量无法有效解决多义词表征,经典神经网络模型无法充分提取语义特征的问题,文中提出了基于BERT(bidirectional encoder representations from transformers)和双向...针对传统情感分析任务中,使用Word2Vec(word to vector)模型生成文本词向量无法有效解决多义词表征,经典神经网络模型无法充分提取语义特征的问题,文中提出了基于BERT(bidirectional encoder representations from transformers)和双向门控循环单元(bidirectional gated recurrent unit,BGRU)以及注意力机制(attention mechanism,Att)的BERT-BGRU-Att中文文本情感分析模型。首先通过BERT预训练模型将中文文本转变为矩阵向量的表示形式,然后建立融合注意力机制的BGRU神经网络对文本信息进行特征提取,最后将信息特征按照不同的权重输入到Softmax分类器进行预测。在网络评论数据集上进行实验,结果显示该模型的预测效果优于相关网络模型,表明该模型在中文文本情感分析任务中的有效性。展开更多
Predicting travel trajectory of vehicles can not only provide personalized services to users,but also have a certain effect on traffic guidance and traffic control.In this paper,we build a Bayonet-Corpus based on the ...Predicting travel trajectory of vehicles can not only provide personalized services to users,but also have a certain effect on traffic guidance and traffic control.In this paper,we build a Bayonet-Corpus based on the context of traffic intersections,and use it to model a traffic network.Besides,Bidirectional Gated Recurrent Unit(Bi-GRU)is used to predict the sequence of traffic intersections in one single trajectory.Firstly,considering that real traffic networks are usually complex and disorder and cannot reflect the higher dimensional relationship among traffic intersections,this paper proposes a new traffic network modeling algorithm based on the context of traffic intersections:inspired by the probabilistic language model,a Bayonet-Corpus is constructed from traffic intersections in real trajectory sequence,so the high-dimensional similarity between corpus nodes can be used to measure the semantic relation of real traffic intersections.This algorithm maps vehicle trajectory nodes into a high-dimensional space vector,blocking complex structure of real traffic network and reconstructing the traffic network space.Then,the bayonets sequence in real traffic network is mapped into a matrix.Considering the trajectories sequence is bidirectional,and Bi-GRU can handle information from forward and backward simultaneously,we use Bi-GRU to bidirectionally model the trajectory matrix for the purpose of prediction.展开更多
Near-infrared(NIR)spectral analysis,which has the advantages of rapidness,nondestruction and high-efficiency,is widely used in the detection of feed,food and mineral.In terms of qualitative identification,it can also ...Near-infrared(NIR)spectral analysis,which has the advantages of rapidness,nondestruction and high-efficiency,is widely used in the detection of feed,food and mineral.In terms of qualitative identification,it can also be used for the discriminant analysis of medicines.Long short-term memory(LSTM)neural network,bidirectional long short-term memory(BiLSTM)neural network and gated recurrent unit(GRU)network are variants of the recurrent neural network(RNN).The potential relationship between nonlinear features learned from the sequence by these variants is used to complete the missions infields such as natural language processing,signal classification and video analysis.Since the effect of these variants in drug identification is still to be studied,this paper constructs a multiclassifier of these three variants,using compoundα-keto acid tablets produced by four manufacturers and repaglinide tablets produced by five manufacturers as the research object.Then,the paper analyzes the impacts of seven different preprocessed methods on the drug NIR data by constructing different layers of LSTM,BiLSTM and GRU networks and compares different classification model indicators and training time of each model.When the spectrum data are pre-processed by z-score normalization,the GRU-3 model has the best accuracy in all models.The BiLSTM models are better for analyzing high coincidence data.The method proposed in this paper can be further extended to other NIR spectroscopy data sets.展开更多
基金This research is partially supported by the National Natural Science Foundation of China(Grant No.61772098)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJZD K201900603,KJQN201900629)Chongqing Grad-uate Education Teaching Reform Project(No.yjg183081).
文摘Predicting travel trajectory of vehicles can not only provide personalized services to users,but also have a certain effect on traffic guidance and traffic control.In this paper,we build a Bayonet-Corpus based on the context of traffic intersections,and use it to model a traffic network.Besides,Bidirectional Gated Recurrent Unit(Bi-GRU)is used to predict the sequence of traffic intersections in one single trajectory.Firstly,considering that real traffic networks are usually complex and disorder and cannot reflect the higher dimensional relationship among traffic intersections,this paper proposes a new traffic network modeling algorithm based on the context of traffic intersections:inspired by the probabilistic language model,a Bayonet-Corpus is constructed from traffic intersections in real trajectory sequence,so the high-dimensional similarity between corpus nodes can be used to measure the semantic relation of real traffic intersections.This algorithm maps vehicle trajectory nodes into a high-dimensional space vector,blocking complex structure of real traffic network and reconstructing the traffic network space.Then,the bayonets sequence in real traffic network is mapped into a matrix.Considering the trajectories sequence is bidirectional,and Bi-GRU can handle information from forward and backward simultaneously,we use Bi-GRU to bidirectionally model the trajectory matrix for the purpose of prediction.
基金This research was supported by the Science and Technology Planning Project of Guangdong Province(Grant Nos.2017B020221002,2018B020207008 and 2021B1111610005)Science and Technology Planning Project of Guangzhou,Grant No.201707010410。
文摘Near-infrared(NIR)spectral analysis,which has the advantages of rapidness,nondestruction and high-efficiency,is widely used in the detection of feed,food and mineral.In terms of qualitative identification,it can also be used for the discriminant analysis of medicines.Long short-term memory(LSTM)neural network,bidirectional long short-term memory(BiLSTM)neural network and gated recurrent unit(GRU)network are variants of the recurrent neural network(RNN).The potential relationship between nonlinear features learned from the sequence by these variants is used to complete the missions infields such as natural language processing,signal classification and video analysis.Since the effect of these variants in drug identification is still to be studied,this paper constructs a multiclassifier of these three variants,using compoundα-keto acid tablets produced by four manufacturers and repaglinide tablets produced by five manufacturers as the research object.Then,the paper analyzes the impacts of seven different preprocessed methods on the drug NIR data by constructing different layers of LSTM,BiLSTM and GRU networks and compares different classification model indicators and training time of each model.When the spectrum data are pre-processed by z-score normalization,the GRU-3 model has the best accuracy in all models.The BiLSTM models are better for analyzing high coincidence data.The method proposed in this paper can be further extended to other NIR spectroscopy data sets.
