From literature surveys to legal document collections, people need to organize and explore large amounts of documents. During these tasks, students and researchers will search for documents based on particular themes. In this paper, we use a popular topic modeling algorithm, Latent Dirichlet Allocation, to derive topic distributions for articles. We allow users to specify personal topic distribution to contextualize the exploration experience. We introduce three types of exploration: user model re-weighted keyword search, topic-based search, and topic-based exploration. We demonstrate these methods using a scientific citation data set and a Wikipedia article collection. We also describe the user interaction model.
Authors:
Christan Grant, Clint P. George, Virupaksha Kanjilal, Supriya Nirkhiwale, Joseph Wilson, Daisy Zhe Wang
Bibtex:
@article{,
author = "Christan Grant, Clint P. George, Virupaksha Kanjilal, Supriya Nirkhiwale, Joseph Wilson, Daisy Zhe Wang",
title = "A Topic-Based Search, Visualization, and Exploration System",
journal = "Proceedings of the 28th International FLAIRS Conference",
year = "2015"
}
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