This paper proposes an emotion judgment system by using an electroencephalogram(EEG)feature concept base with premise of noises included.This method references the word concept association system,which associates one ...This paper proposes an emotion judgment system by using an electroencephalogram(EEG)feature concept base with premise of noises included.This method references the word concept association system,which associates one word with other plural words and decides the relationship between several words.In this proposed emotion judgment system,the source EEG is input and 42 EEG features are constructed by EEG data;the data are then calculated by spectrum analysis and normalization.All 2945 EEG data of 4 emotions in the EEG data emotion knowledge base are calculated by the degree of association for getting the nearest EEG data from the EEG feature concept base constructed by 2844 concepts.From the experiment,the accuracy of the proposed system was 55.9%,which was higher than the support vector machine(SVM)method.As this result,the chain structured feature of the EEG feature concept base and the efficiency by the calculation of degree of association for EEG data help reduce the influence of the noise.展开更多
在基于语义的查询扩展中,为了找到描述查询需求语义的相关概念,词语.概念相关度的计算是语义查询扩展中的关键一步.针对词语.概念相关度的计算,提出一种K2CM(keyword to concept method)方法.K2CM方法从词语.文档.概念所属程度和词语....在基于语义的查询扩展中,为了找到描述查询需求语义的相关概念,词语.概念相关度的计算是语义查询扩展中的关键一步.针对词语.概念相关度的计算,提出一种K2CM(keyword to concept method)方法.K2CM方法从词语.文档.概念所属程度和词语.概念共现程度两个方面来计算词语.概念相关度问语.文档.概念所属程度来源于标注的文档集中词语对概念的所属关系,即词语出现在若干文档中而文档被标注了若干概念.词语.概念共现程度是在词语概念对的共现性基础上增加了词语概念对的文本距离和文档分布特征的考虑.3种不同类型数据集上的语义检索实验结果表明,与传统方法相比,基于K2CM的语义查询扩展可以提高查询效果.展开更多
基金supported by Japan Society for the Promotion of Science(JSPS,16K00311).
文摘This paper proposes an emotion judgment system by using an electroencephalogram(EEG)feature concept base with premise of noises included.This method references the word concept association system,which associates one word with other plural words and decides the relationship between several words.In this proposed emotion judgment system,the source EEG is input and 42 EEG features are constructed by EEG data;the data are then calculated by spectrum analysis and normalization.All 2945 EEG data of 4 emotions in the EEG data emotion knowledge base are calculated by the degree of association for getting the nearest EEG data from the EEG feature concept base constructed by 2844 concepts.From the experiment,the accuracy of the proposed system was 55.9%,which was higher than the support vector machine(SVM)method.As this result,the chain structured feature of the EEG feature concept base and the efficiency by the calculation of degree of association for EEG data help reduce the influence of the noise.
文摘在基于语义的查询扩展中,为了找到描述查询需求语义的相关概念,词语.概念相关度的计算是语义查询扩展中的关键一步.针对词语.概念相关度的计算,提出一种K2CM(keyword to concept method)方法.K2CM方法从词语.文档.概念所属程度和词语.概念共现程度两个方面来计算词语.概念相关度问语.文档.概念所属程度来源于标注的文档集中词语对概念的所属关系,即词语出现在若干文档中而文档被标注了若干概念.词语.概念共现程度是在词语概念对的共现性基础上增加了词语概念对的文本距离和文档分布特征的考虑.3种不同类型数据集上的语义检索实验结果表明,与传统方法相比,基于K2CM的语义查询扩展可以提高查询效果.
基金国家重点基础研究发展规划(973)(the National Grand Fundamental Research 973 Program of China under Grant No.2004CB318000)辽宁省教育厅资助科研课题(the Research Project of Department of Education of Liaoning Province China)
