There are many interrogative sentences in both modern Chinese and English languages,but they are frequently used in non-interrogative ways,their meanings are more than meets the eye.Some people put such kind of non-in...There are many interrogative sentences in both modern Chinese and English languages,but they are frequently used in non-interrogative ways,their meanings are more than meets the eye.Some people put such kind of non-interrogative sentence pattern into rhetoric question category,it is not right.Its meaning is beyond the boundary of rhetoric question pattern.Moreover,in both Chinese and English languages,there are similarities and differencialities in non-interrogative use in interrogative sentence patterns.This paper deals with the non-interrogative use in interrogative sentence patterns in a pragmatic analysis perspective,so as to give a deep understanding of it.展开更多
How to predict the bus arrival time accurately is a crucial problem to be solved in Internet of Vehicle. Existed methods cannot solve the problem effectively for ignoring the traffic delay jitter. In this paper,a thre...How to predict the bus arrival time accurately is a crucial problem to be solved in Internet of Vehicle. Existed methods cannot solve the problem effectively for ignoring the traffic delay jitter. In this paper,a three-stage mixed model is proposed for bus arrival time prediction. The first stage is pattern training. In this stage,the traffic delay jitter patterns(TDJP)are mined by K nearest neighbor and K-means in the historical traffic time data. The second stage is the single-step prediction,which is based on real-time adjusted Kalman filter with a modification of historical TDJP. In the third stage,as the influence of historical law is increasing in long distance prediction,we combine the single-step prediction dynamically with Markov historical transfer model to conduct the multi-step prediction. The experimental results show that the proposed single-step prediction model performs better in accuracy and efficiency than short-term traffic flow prediction and dynamic Kalman filter. The multi-step prediction provides a higher level veracity and reliability in travel time forecasting than short-term traffic flow and historical traffic pattern prediction models.展开更多
随着网络中多元化、碎片化的文本数量增多,传统模型对此类文本进行情感分析时,存在长距离语义信息挖掘不够充分、深层次的特征提取不够完整的问题。为解决上述问题,提出了基于ALBERT-HACNN-TUP(A self-supervised learning model based ...随着网络中多元化、碎片化的文本数量增多,传统模型对此类文本进行情感分析时,存在长距离语义信息挖掘不够充分、深层次的特征提取不够完整的问题。为解决上述问题,提出了基于ALBERT-HACNN-TUP(A self-supervised learning model based on a Lite BERT and text universal pooling a hierarchical attention convolutional neural network)的情感分析模型。模型首先使用ALBERT预训练语言模型提取更长距离的语义信息;其次改进CNN的卷积层,提出了一种分层注意力卷积神经网络(HACNN),根据卷积层提取特征信息的重要程度进行动态权重调整,进一步突出文本的情感极性词;再利用池化层Text Universal Pooling(TUP)动态学习池化权重,对不同通道进行提取和融合,最大程度保留了文本更深层次的情感特征,尤其对含有复杂语义的反讽文本有更好的效果。在不同数据集上进行了实验。仿真结果表明,上述模型提高了运行效率,具有良好的泛化性与精确度。展开更多
文摘There are many interrogative sentences in both modern Chinese and English languages,but they are frequently used in non-interrogative ways,their meanings are more than meets the eye.Some people put such kind of non-interrogative sentence pattern into rhetoric question category,it is not right.Its meaning is beyond the boundary of rhetoric question pattern.Moreover,in both Chinese and English languages,there are similarities and differencialities in non-interrogative use in interrogative sentence patterns.This paper deals with the non-interrogative use in interrogative sentence patterns in a pragmatic analysis perspective,so as to give a deep understanding of it.
基金National Science and Technology Major Project(2016ZX03001025-003)Special Found for Beijing Common Construction Project
文摘How to predict the bus arrival time accurately is a crucial problem to be solved in Internet of Vehicle. Existed methods cannot solve the problem effectively for ignoring the traffic delay jitter. In this paper,a three-stage mixed model is proposed for bus arrival time prediction. The first stage is pattern training. In this stage,the traffic delay jitter patterns(TDJP)are mined by K nearest neighbor and K-means in the historical traffic time data. The second stage is the single-step prediction,which is based on real-time adjusted Kalman filter with a modification of historical TDJP. In the third stage,as the influence of historical law is increasing in long distance prediction,we combine the single-step prediction dynamically with Markov historical transfer model to conduct the multi-step prediction. The experimental results show that the proposed single-step prediction model performs better in accuracy and efficiency than short-term traffic flow prediction and dynamic Kalman filter. The multi-step prediction provides a higher level veracity and reliability in travel time forecasting than short-term traffic flow and historical traffic pattern prediction models.
文摘随着网络中多元化、碎片化的文本数量增多,传统模型对此类文本进行情感分析时,存在长距离语义信息挖掘不够充分、深层次的特征提取不够完整的问题。为解决上述问题,提出了基于ALBERT-HACNN-TUP(A self-supervised learning model based on a Lite BERT and text universal pooling a hierarchical attention convolutional neural network)的情感分析模型。模型首先使用ALBERT预训练语言模型提取更长距离的语义信息;其次改进CNN的卷积层,提出了一种分层注意力卷积神经网络(HACNN),根据卷积层提取特征信息的重要程度进行动态权重调整,进一步突出文本的情感极性词;再利用池化层Text Universal Pooling(TUP)动态学习池化权重,对不同通道进行提取和融合,最大程度保留了文本更深层次的情感特征,尤其对含有复杂语义的反讽文本有更好的效果。在不同数据集上进行了实验。仿真结果表明,上述模型提高了运行效率,具有良好的泛化性与精确度。
