The Journal of Machine Learning Research
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A probabilistic approach to spatiotemporal theme pattern mining on weblogs
Proceedings of the 15th international conference on World Wide Web
Automatically labeling hierarchical clusters
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Diverse Topic Phrase Extraction through Latent Semantic Analysis
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Automatic labeling of multinomial topic models
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion integration through semi-supervised topic modeling
Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Best topic word selection for topic labelling
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Automatic labelling of topic models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A hierarchical model of web summaries
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
A phrase mining framework for recursive construction of a topical hierarchy
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Recently, statistical topic modeling has been widely applied in text mining and knowledge management due to its powerful ability. A topic, as a probability distribution over words, is usually difficult to be understood. A common, major challenge in applying such topic models to other knowledge management problem is to accurately interpret the meaning of each topic. Topic labeling, as a major interpreting method, has attracted significant attention recently. However, previous works simply treat topics individually without considering the hierarchical relation among topics, and less attention has been paid to creating a good hierarchical topic descriptors for a hierarchy of topics. In this paper, we propose two effective algorithms that automatically assign concise labels to each topic in a hierarchy by exploiting sibling and parent-child relations among topics. The experimental results show that the inter-topic relation is effective in boosting topic labeling accuracy and the proposed algorithms can generate meaningful topic labels that are useful for interpreting the hierarchical topics.