Topic representation: Finding more representative words in topic models

doi:10.1016/j.patrec.2019.01.018

CIOMP OpenIR

	Topic representation: Finding more representative words in topic models
	J.J.Chi; J.H.Ouyang; C.C.Li; X.Y.Dong; X.M.Li; X.H.Wang
	2019
发表期刊	Pattern Recognition Letters
ISSN	0167-8655
卷号	123 页码:53-60
摘要	The top word list, i.e., the top-M words with highest marginal probabilities in a given topic, is the standard topic representation in topic models. Most of recent automatical topic labeling algorithms and popular topic quality metrics are based on it. However, we find, empirically, words in this type of top word list are not always representative. The objective of this paper is to find more representative top word lists for topics. To achieve this, we rerank the words in a given topic by further considering marginal probabilities on words over every other topic. The reranking list of top-M words is used to be a novel topic representation for topic models. We investigate three reranking methodologies, using (1) standard deviation weight, (2) standard deviation weight with topic size and (3) Chi Square chi(2) statistic selection. Experimental results on real-world collections indicate that our representations can extract more representative words for topics, agreeing with human judgements. (C) 2019 Elsevier B.V. All rights reserved.
关键词	Topic modeling,Topic representation,Topical word representation,Reranking methodology,Computer Science
DOI	10.1016/j.patrec.2019.01.018
URL	查看原文
收录类别	SCI ; EI
语种	英语
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/63430
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	J.J.Chi,J.H.Ouyang,C.C.Li,et al. Topic representation: Finding more representative words in topic models[J]. Pattern Recognition Letters,2019,123:53-60.
APA	J.J.Chi,J.H.Ouyang,C.C.Li,X.Y.Dong,X.M.Li,&X.H.Wang.(2019).Topic representation: Finding more representative words in topic models.Pattern Recognition Letters,123,53-60.
MLA	J.J.Chi,et al."Topic representation: Finding more representative words in topic models".Pattern Recognition Letters 123(2019):53-60.

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