Publication Type

Conference Proceeding Article

Publication Date



Classical approaches to visualization directly reduce a document's high-dimensional representation into visualizable two or three dimensions, using techniques such as multidimensional scaling. More recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. We call the latter semantic visualization problem, as it seeks to jointly model topic and visualization. While previous approaches aim to preserve the global consistency, they do not consider the local consistency in terms of the intrinsic geometric structure of the document manifold. We therefore propose an unsupervised probabilistic model, called Semafore, which aims to preserve the manifold in the lower-dimensional spaces. Comprehensive experiments on several real-life text datasets of news articles and web pages show that Semafore significantly outperforms the state-of-the-art baselines on objective evaluation metrics.


visualization, topic modeling, manifold, regularization


Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics


Proceedings of the 28th AAAI Conference on Artificial Intelligence: July 27-31, 2014, Québec City

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AAAI Press

City or Country

Menlo Park, CA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. (324 kB)
This is an implementation of SEMAFORE - a semantic visualization method from Le & Lauw (AAAI 2014, JAIR 2016).

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