作者机构：
华中师范大学计算机学院,武汉430079;国家语言资源监测与研究中心网络媒体语言分中心,武汉430079;[何婷婷; 涂新辉; 李芳; 王建文] School of Computer Science, Huazhong Normal University, Wuhan 430079, China, Network Media Branch, National Language Resources Monitoring and Research Center, Wuhan 430079, China
通讯机构：
[Tu, X.] S;School of Computer Science, Huazhong Normal University, China
作者机构：
[王华; 刘耀林] School of Resource and Environment Science, Wuhan University, Wuhan, 430079, China;[姬盈利] Department of Computer Science, Central China Normal University, Wuhan 430079, China;[王华; 刘耀林] Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
通讯机构：
School of Resource and Environment Science, Wuhan University, China
作者机构：
[He Tingting] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.;[Li Fang] Cent China Normal Univ, Engn & Res Ctr Informat Technol Educ, Wuhan 430079, Peoples R China.;[He Tingting; Li Fang] Natl Language Resources Monitoring & Res Ctr, Network Media Branch, Wuhan 430079, Peoples R China.
通讯机构：
[He Tingting] C;Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
摘要：
This paper focuses on semantic knowledge acquisition from blogs with the proposed tagtopic model. The model extends the Latent Dirichlet Allocation (LDA) model by adding a tag layer between the document and the topic. Each document is represented by a mixture of tags; each tag is associated with a multinomial distribution over topics and each topic is associated with a multinomial distribution over words. After parameter estimation, the tags are used to describe the underlying topics. Thus the latent semantic knowledge within the topics could be represented explicitly. The tags are treated as concepts, and the top-TV words from the top topics are selected as related words of the concepts. Then PMI-IR is employed to compute the relatedness between each tag-word pair and noisy words with low correlation removed to improve the quality of the semantic knowledge. Experiment results show that the proposed method can effectively capture semantic knowledge, especially the polyseme and synonym.
作者机构：
[高戈; 陈怡; 胡瑞敏] National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, Hubei 430072, China;[陈怡] Department of Computer, Huazhong Normal University, Wuhan, Hubei 430079, China
通讯机构：
National Engineering Research Center for Multimedia Software, Wuhan University, China
作者机构：
[游林; 张帆; 桑永宣] College of Communication School, Hangzhou Dianzi University, Hangzhou 310018, China;[陈曙] Department of Computer Science, Huazhong Normal University, Wuhan 430079, China
通讯机构：
College of Communication School, Hangzhou Dianzi University, China
作者机构：
[李宏伟; 付丽华] School of Mathematics and Physics, China University of Geosciences, Wuhan, Hubei 430074, China;[张猛] Department of Computer, Central China Normal University, Wuhan, Hubei 430079, China
通讯机构：
School of Mathematics and Physics, China University of Geosciences, China
作者机构：
[陈怡] Department of Computer Science, Huazhong Normal University, Wuhan 430079, China;[高戈; 胡瑞敏] School of Computer, Wuhan University, Wuhan 430072, China
通讯机构：
Department of Computer Science, Huazhong Normal University, China
关键词：
协作;任务;效用
摘要：
近年来基于Ad Hoc网络的智能化视频监控等新型网络应用逐渐兴起和普及,该类应用需要考虑多个数据源的联合协作．但是现有Ad Hoc网络的资源分配机制主要针对每个独立的数据流,在实现资源分配过程中没有考虑不同数据之间的依赖关系,导致针对联合任务的资源利用效率不高,成为制约网络服务质量提高的重要因素之一．文中首次把基于任务的评价参数纳入到无线Ad Hoc:网络最优分配模型中,使得资源分配模型能够描述构成任务的各数据流之间的依赖关系．提出了基于任务利用率的动态资源调节算法(Mission utility based Resource Dynamic algorithm,MRD)．证明了该算法满足非线性问题最优约束,所求数据源发送速率满足系统全局最优．仿真结果表明,提出的MRRD算法能够提高多数据源协作Ad Hoc 网络利用效率．
作者机构：
[吴晓平; 付钰; 叶清] Department of Information Security, Naval University of Engineering, Hubei Wuhan 430033, China;[彭熙] Department of Computer Science, Huazhong Normal University, Hubei Wuhan 430079, China
通讯机构：
Department of Information Security, Naval University of Engineering, China
