介绍下一代美国GRACE Follow-On和欧洲E.MOTION(Earth System Mass Transport Mission)卫星重力测量计划的研究进展,以期为我国下一代Post-GRACE卫星重力测量工程的成功实施提供参考依据。详细阐述了我国下一代Post-GRACE卫星重力测量...介绍下一代美国GRACE Follow-On和欧洲E.MOTION(Earth System Mass Transport Mission)卫星重力测量计划的研究进展,以期为我国下一代Post-GRACE卫星重力测量工程的成功实施提供参考依据。详细阐述了我国下一代Post-GRACE卫星重力测量工程的研究意义和科学目标,建议我国先期构建卫星精密定轨和卫星重力反演仿真模拟软件平台系统,开展重力卫星关键载荷误差分析研究,以及执行卫星重力测量任务需求。展开更多
In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined...In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories： joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields（CRFs） on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.展开更多
文摘介绍下一代美国GRACE Follow-On和欧洲E.MOTION(Earth System Mass Transport Mission)卫星重力测量计划的研究进展,以期为我国下一代Post-GRACE卫星重力测量工程的成功实施提供参考依据。详细阐述了我国下一代Post-GRACE卫星重力测量工程的研究意义和科学目标,建议我国先期构建卫星精密定轨和卫星重力反演仿真模拟软件平台系统,开展重力卫星关键载荷误差分析研究,以及执行卫星重力测量任务需求。
基金supported by the Ministry of Education,Science,Sports and Culture,Grant-in-Aid for Scientific Research under Grant No.22240021the Grant-in-Aid for Challenging Exploratory Research under Grant No.21650030
文摘In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories： joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields（CRFs） on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.
