A Topic-Based Search, Visualization, and Exploration System
Christan Grant, Clint P. George, Virupaksha Kanjilal, Supriya Nirkhiwale, Joseph N. Wilson, Daisy Zhe Wang · 2015
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 Alloca-tion, to derive topic distributions for articles. We allow users to specify personal topic distribution to contextualize the ex-ploration 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 col-lection. We also describe the user interaction model. 1