Labelling Topics in Weibo Using Word Embedding and Graph-based Method

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 互联网大数据与安全信息学研究中心

	Labelling Topics in Weibo Using Word Embedding and Graph-based Method
	Zhipeng Jin1 ; Qiudan Li1 ; Can Wang1 ; Daniel D. Zeng1,2 ; Lei Wang1
	2016
会议日期	20-22 April 2016
会议地点	USA
关键词	Weibo Microblogs Deep Learning Labelling Topics Graph
页码	34-37
英文摘要	Nowadays, in China, Weibo is becoming an increasingly popular way for people to know what is happening in the world. Labelling topics is of much importance for better understanding the semantics of topics. Existing works mainly focus on deriving candidate labels by exploring the use of external knowledge, which may be more appropriate for well formatted and static documents. Recently, it has been a new trend to generate labels for sparse and dynamic microblogging environment using summarization method. The challenges of labelling topics are how to obtain coherent candidate labels and how to rank the labels. In this paper, based on the latest research work in deep learning, we propose a novel and unified model for labelling topics in Weibo, which firstly adopts word embedding and clustering method to learn dense semantic representation of topic words and mine the coherent candidate topic labels, then, generates interpretable labels using a graph-based model. Experimental results show that topics labels discovered by our model not only have high topic coherence, but also are meaningful and interpretable.
语种	英语
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/20069]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
作者单位	1.The State Key Laboratory of Management and Control for Complex Systems Institute of Automation, Chinese Academy of Sciences Beijing 100190, China 2.Department of Management Information Systems University of Arizona Tucson, Arizona, USA
推荐引用方式 GB/T 7714	Zhipeng Jin,Qiudan Li,Can Wang,et al. Labelling Topics in Weibo Using Word Embedding and Graph-based Method[C]. 见:. USA. 20-22 April 2016.