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

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Labelling Topics in Weibo Using Word Embedding and Graph-based Method
	Zhipeng Jin1 ; Qiudan Li1 ; Can Wang1 ; Daniel D. Zeng1,2 ; Lei Wang1
	2016
会议名称	Labelling Topics in Weibo Using Word Embedding and Graph-based Method
页码	34-37
会议日期	20-22 April 2016
会议地点	USA
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.
关键词	Weibo Microblogs Deep Learning Labelling Topics Graph
收录类别	EI
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/20069
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
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
第一作者单位	中国科学院自动化研究所
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],2016:34-37.

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