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	Tweet Timeline Generation via Graph-Based Dynamic Greedy Clustering
	Fan, Feifan ; Qiang, Runwei ; Lv, Chao ; Zhao, Wayne Xin ; Yang, Jianwu
	2015
关键词	Tweet timeline generation Graph-based dynamic greedy clustering Tweet embedding
英文摘要	When searching a query in the microblogging, a user would typically receive an archive of tweets as part of a retrospective piece on the impact of social media. For ease of understanding the retrieved tweets, it is useful to produce a summarized timeline about a given topic. However, tweet timeline generation is quite challenging due to the noisy and temporal characteristics of microblogs. In this paper, we propose a graph-based dynamic greedy clustering approach, which considers the coverage, relevance and novelty of the tweet timeline. First, tweet embedding representation is learned in order to construct the tweet semantic graph. Based on the graph, we estimate the coverage of timeline according to the graph connectivity. Furthermore, we integrate a noise tweet elimination component to remove noisy tweets with the lexical and semantic features based on relevance and novelty. Experimental results on public Text Retrieval Conference (TREC) Twitter corpora demonstrate the effectiveness of the proposed approach.; EI; CPCI-S(ISTP); fanff@pku.edu.cn; qiangrw@pku.edu.cn; lvchao@pku.edu.cn; batmanfly@gmail.com; yangjw@pku.edu.cn; 304-316; 9460
语种	英语
出处	INFORMATION RETRIEVAL TECHNOLOGY, AIRS 2015
DOI标识	10.1007/978-3-319-28940-3_24
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436961]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Fan, Feifan,Qiang, Runwei,Lv, Chao,et al. Tweet Timeline Generation via Graph-Based Dynamic Greedy Clustering. 2015-01-01.

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