Concept decompositions for large sparse text data using clustering
Machine Learning
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The Journal of Machine Learning Research
Automatic labelling of topic models
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling
ACM Transactions on Intelligent Systems and Technology (TIST)
DIRECTions: design and specification of an IR evaluation infrastructure
CLEF'12 Proceedings of the Third international conference on Information Access Evaluation: multilinguality, multimodality, and visual analytics
Event identification in web social media through named entity recognition and topic modeling
Data & Knowledge Engineering
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This paper explores visualizations of document collections, which we call topic maps. Our topic maps are based on a topic model of the document collection, where the topic model is used to determine the semantic content of each document. Using two collections of search results, we show how topic maps reveal the semantic structure of a collection and visually communicate the diversity of content in the collection. We describe techniques for assessing the validity and accuracy of topic maps, and discuss the challenge of producing useful two-dimensional maps of documents.