QQ客服 官方微博 反馈留言

	SSHLDA: A semi-supervised hierarchical topic model
	Mao, Xian-Ling ; Ming, Zhao-Yan ; Chua, Tat-Seng ; Li, Si ; Yan, Hongfei ; Li, Xiaoming
	2012
英文摘要	Supervised hierarchical topic modeling and unsupervised hierarchical topic modeling are usually used to obtain hierarchical topics, such as hLLDA and hLDA. Supervised hierarchical topic modeling makes heavy use of the information from observed hierarchical labels, but cannot explore new topics; while unsu-pervised hierarchical topic modeling is able to detect automatically new topics in the data space, but does not make use of any information from hierarchical labels. In this paper, we propose a semi-supervised hierarchical topic model which aims to explore new topics automatically in the data space while incorporating the information from observed hierarchical labels into the modeling process, called Semi-Supervised Hierarchical Latent Dirichlet Allocation (SSHLDA). We also prove that hLDA and hLLDA are special cases of SSHLDA. We conduct experiments on Yahoo! Answers and ODP datasets, and assess the performance in terms of perplexity and clustering. The experimental results show that predictive ability of SSHLDA is better than that of baselines, and SSHLDA can also achieve significant improvement over baselines for clustering on the FScore measure. ? 2012 Association for Computational Linguistics.; EI; 0
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/294534]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Mao, Xian-Ling,Ming, Zhao-Yan,Chua, Tat-Seng,et al. SSHLDA: A semi-supervised hierarchical topic model. 2012-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。