Nowadays, millions of users use many social media systems every day. These services produce massive messages, which play a vital role in the social networking paradigm. As we see, an intelligent learning emotion syste...Nowadays, millions of users use many social media systems every day. These services produce massive messages, which play a vital role in the social networking paradigm. As we see, an intelligent learning emotion system is desperately needed for detecting emotion among these messages. This system could be suitable in understanding users’ feelings towards particular discussion. This paper proposes a text-based emotion recognition approach that uses personal text data to recognize user’s current emotion. The proposed approach applies Dominant Meaning Technique to recognize user’s emotion. The paper reports promising experiential results on the tested dataset based on the proposed algorithm.展开更多
In the study of recommendation systems,many methods based on predicting ratings have been put forward.However,the rating-predicting methods have some shortages.It pays too much attention to predicting,instead of the n...In the study of recommendation systems,many methods based on predicting ratings have been put forward.However,the rating-predicting methods have some shortages.It pays too much attention to predicting,instead of the nature of recommendation,which is predicting the order of ratings.Thus,we use a pairwise-based learning algorithm to learn our model and take the zero-sampling method to improve our model.In addition,we propose a text modeling method making the recommendations more explicable.It is proved that our system performs better than other state-of-art展开更多
Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information,the document classication task becomes an interesting area for ...Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information,the document classication task becomes an interesting area for controlling data behavior.This paper presents a document classication multimodal for categorizing textual semi-structured and unstructured documents.The multimodal implements several individual deep learning models such as Deep Neural Networks(DNN),Recurrent Convolutional Neural Networks(RCNN)and Bidirectional-LSTM(Bi-LSTM).The Stacked Ensemble based meta-model technique is used to combine the results of the individual classiers to produce better results,compared to those reached by any of the above mentioned models individually.A series of textual preprocessing steps are executed to normalize the input corpus followed by text vectorization techniques.These techniques include using Term Frequency Inverse Term Frequency(TFIDF)or Continuous Bag of Word(CBOW)to convert text data into the corresponding suitable numeric form acceptable to be manipulated by deep learning models.Moreover,this proposed model is validated using a dataset collected from several spaces with a huge number of documents in every class.In addition,the experimental results prove that the proposed model has achieved effective performance.Besides,upon investigating the PDF Documents classication,the proposed model has achieved accuracy up to 0.9045 and 0.959 for the TFIDF and CBOW features,respectively.Moreover,concerning the JSON Documents classication,the proposed model has achieved accuracy up to 0.914 and 0.956 for the TFIDF and CBOW features,respectively.Furthermore,as for the XML Documents classication,the proposed model has achieved accuracy values up to 0.92 and 0.959 for the TFIDF and CBOW features,respectively.展开更多
Recent text generation methods frequently learn node representations from graph‐based data via global or local aggregation,such as knowledge graphs.Since all nodes are connected directly,node global representation en...Recent text generation methods frequently learn node representations from graph‐based data via global or local aggregation,such as knowledge graphs.Since all nodes are connected directly,node global representation encoding enables direct communication between two distant nodes while disregarding graph topology.Node local representation encoding,which captures the graph structure,considers the connections between nearby nodes but misses out onlong‐range relations.A quantum‐like approach to learning bettercontextualised node embeddings is proposed using a fusion model that combines both encoding strategies.Our methods significantly improve on two graph‐to‐text datasets compared to state‐of‐the‐art models in various experiments.展开更多
目的探讨分析以病例为基础的教学(case based learning,CBL)联合传统教学模式在脑血管病5年制临床医学实习医师带教中的效果。方法以2015年9月-2016年9月于首都医科大学附属北京潞河医院神经内科脑血管病教学组5年制临床医学实习医师60...目的探讨分析以病例为基础的教学(case based learning,CBL)联合传统教学模式在脑血管病5年制临床医学实习医师带教中的效果。方法以2015年9月-2016年9月于首都医科大学附属北京潞河医院神经内科脑血管病教学组5年制临床医学实习医师60例作为教学对象,按照1∶1比例随机分入传统教学模式组、CBL联合传统教学模式组。教学结束后以基础理论知识考核、实践考核(包括查体及病例分析)以及问卷调查等方式进行评价。结果两组的基础理论知识考核成绩无显著性差异;CBL联合传统教学组神经系统体格检查及病例分析成绩显著高于对照组[(89.10±2.99)分vs(86.60±3.41)分,P=0.015];CBL联合传统教学组学生在自我评价是否增加了学习兴趣、学习主动能力、文献查阅能力、综合分析问题的能力、知识拓展能力、团队合作能力、教学满意度及系统诊断能力等方面均显著优于对照组。结论 CBL联合传统教学模式可以有效提高实习医师掌握脑血管病临床诊疗能力。展开更多
由于算法的简单和效果的出色,Na ve Bayes被广泛地应用到了垃圾邮件过滤当中。通过理论与实验分析发现,结构差异较大的邮件集特征分布差异也较大,这种特征分布差异影响到了Na ve Bayes算法的效果。在此基础上,论文提出了一种基于结构特...由于算法的简单和效果的出色,Na ve Bayes被广泛地应用到了垃圾邮件过滤当中。通过理论与实验分析发现,结构差异较大的邮件集特征分布差异也较大,这种特征分布差异影响到了Na ve Bayes算法的效果。在此基础上,论文提出了一种基于结构特征的双层过滤模型,对不同结构的邮件使用不同的Na ve Bayes分类器分开训练和学习。实验分析表明,Na ve Bayes使用该模型之后效果有明显的提高,已经与SVM非常接近。展开更多
文摘Nowadays, millions of users use many social media systems every day. These services produce massive messages, which play a vital role in the social networking paradigm. As we see, an intelligent learning emotion system is desperately needed for detecting emotion among these messages. This system could be suitable in understanding users’ feelings towards particular discussion. This paper proposes a text-based emotion recognition approach that uses personal text data to recognize user’s current emotion. The proposed approach applies Dominant Meaning Technique to recognize user’s emotion. The paper reports promising experiential results on the tested dataset based on the proposed algorithm.
文摘In the study of recommendation systems,many methods based on predicting ratings have been put forward.However,the rating-predicting methods have some shortages.It pays too much attention to predicting,instead of the nature of recommendation,which is predicting the order of ratings.Thus,we use a pairwise-based learning algorithm to learn our model and take the zero-sampling method to improve our model.In addition,we propose a text modeling method making the recommendations more explicable.It is proved that our system performs better than other state-of-art
文摘Due to the availability of a huge number of electronic text documents from a variety of sources representing unstructured and semi-structured information,the document classication task becomes an interesting area for controlling data behavior.This paper presents a document classication multimodal for categorizing textual semi-structured and unstructured documents.The multimodal implements several individual deep learning models such as Deep Neural Networks(DNN),Recurrent Convolutional Neural Networks(RCNN)and Bidirectional-LSTM(Bi-LSTM).The Stacked Ensemble based meta-model technique is used to combine the results of the individual classiers to produce better results,compared to those reached by any of the above mentioned models individually.A series of textual preprocessing steps are executed to normalize the input corpus followed by text vectorization techniques.These techniques include using Term Frequency Inverse Term Frequency(TFIDF)or Continuous Bag of Word(CBOW)to convert text data into the corresponding suitable numeric form acceptable to be manipulated by deep learning models.Moreover,this proposed model is validated using a dataset collected from several spaces with a huge number of documents in every class.In addition,the experimental results prove that the proposed model has achieved effective performance.Besides,upon investigating the PDF Documents classication,the proposed model has achieved accuracy up to 0.9045 and 0.959 for the TFIDF and CBOW features,respectively.Moreover,concerning the JSON Documents classication,the proposed model has achieved accuracy up to 0.914 and 0.956 for the TFIDF and CBOW features,respectively.Furthermore,as for the XML Documents classication,the proposed model has achieved accuracy values up to 0.92 and 0.959 for the TFIDF and CBOW features,respectively.
基金supported by the National Natural Science Foundation of China under Grant(62077015)the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province,Zhejiang Normal University,Zhejiang,China,the Key Research and Development Program of Zhejiang Province(No.2021C03141)the National Key R&D Program of China under Grant(2022YFC3303600).
文摘Recent text generation methods frequently learn node representations from graph‐based data via global or local aggregation,such as knowledge graphs.Since all nodes are connected directly,node global representation encoding enables direct communication between two distant nodes while disregarding graph topology.Node local representation encoding,which captures the graph structure,considers the connections between nearby nodes but misses out onlong‐range relations.A quantum‐like approach to learning bettercontextualised node embeddings is proposed using a fusion model that combines both encoding strategies.Our methods significantly improve on two graph‐to‐text datasets compared to state‐of‐the‐art models in various experiments.
文摘目的探讨分析以病例为基础的教学(case based learning,CBL)联合传统教学模式在脑血管病5年制临床医学实习医师带教中的效果。方法以2015年9月-2016年9月于首都医科大学附属北京潞河医院神经内科脑血管病教学组5年制临床医学实习医师60例作为教学对象,按照1∶1比例随机分入传统教学模式组、CBL联合传统教学模式组。教学结束后以基础理论知识考核、实践考核(包括查体及病例分析)以及问卷调查等方式进行评价。结果两组的基础理论知识考核成绩无显著性差异;CBL联合传统教学组神经系统体格检查及病例分析成绩显著高于对照组[(89.10±2.99)分vs(86.60±3.41)分,P=0.015];CBL联合传统教学组学生在自我评价是否增加了学习兴趣、学习主动能力、文献查阅能力、综合分析问题的能力、知识拓展能力、团队合作能力、教学满意度及系统诊断能力等方面均显著优于对照组。结论 CBL联合传统教学模式可以有效提高实习医师掌握脑血管病临床诊疗能力。
文摘由于算法的简单和效果的出色,Na ve Bayes被广泛地应用到了垃圾邮件过滤当中。通过理论与实验分析发现,结构差异较大的邮件集特征分布差异也较大,这种特征分布差异影响到了Na ve Bayes算法的效果。在此基础上,论文提出了一种基于结构特征的双层过滤模型,对不同结构的邮件使用不同的Na ve Bayes分类器分开训练和学习。实验分析表明,Na ve Bayes使用该模型之后效果有明显的提高,已经与SVM非常接近。
