所属领域：
印太交汇区海洋物质能量中心形成演化过程与机制
资源类型：
人工智能与海洋大数据
/ 海洋大数据
文献作者：
Liu, Weidong; Luo, Xiangfeng; Gong, Zhiguo; Xuan, Junyu; Kou, Ngai Meng; Xu, Zheng
文献发表年份：
2016
文献期刊：
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
文献摘要：As social media is opening up such as Twitter and Sina Weibo,(1) large volumes of short texts are flooding on the Web. The ocean of short texts dilutes the limited core semantics of event in cyberspace by redundancy, noises and irrelevant content on the web, which make it difficult to discover the core semantics of event. The major challenges include how to efficiently learn the semantic association distribution by small-scale association relations and how to maximize the coverage of the semantic association distribution by the minimum number of redundancy-free short texts. To solve the above issues, we explore a Markov random field based method for discovering the core semantics of event. This method makes semantics collaborative computation for learning association relation distribution and makes information gradient computation for discovering k redundancy-free texts as the core semantics of event. We evaluate our method by comparing with two state-of-the-art methods on the TAC dataset and the microblog dataset. The results show our method outperforms other methods in extracting core semantics accurately and efficiently. The proposed method can be applied to short text automatic generation, event discovery and summarization for big data analysis. (C) 2015 Elsevier B.V. All rights reserved.
文献类型： Article
文献语种： English
关键词： Core semantics; Semantic link network; Information gradient
文献基金资助： National Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61471232]
文献作者地址： [Liu, Weidong; Luo, Xiangfeng; Xuan, Junyu] Shanghai Univ, Shanghai, Peoples R China; [Gong, Zhiguo; Kou, Ngai Meng] Univ Macau, Macau, Peoples R China; [Xu, Zheng] Minist Publ Secur, Res Inst 3, Shanghai, Peoples R China
