Labelling Topics in Weibo Using Word Embedding and Graph-based Method
Zhipeng Jin1; Qiudan Li1; Can Wang1; Daniel D. Zeng1,2; Lei Wang1
2016
Conference NameLabelling Topics in Weibo Using Word Embedding and Graph-based Method
Pages34-37
Conference Date20-22 April 2016
Conference PlaceUSA
AbstractNowadays, 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. 
KeywordWeibo Microblogs Deep Learning Labelling Topics Graph
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20069
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Affiliation1.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
Recommended Citation
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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