Concept over time: the combination of probabilistic topic model with wikipedia knowledge

Abstract

Probabilistic topic models could be used to extract low-dimension aspects from document collections, and capture how the aspects change over time. However, such models without any human knowledge often produce aspects that are not interpretable. In recent years, a number of knowledge-based topic models and dynamic topic models have been proposed, but they could not process concept knowledge and temporal information in Wikipedia. In this paper, we fill this gap by proposing a new probabilistic modeling framework which combines both data-driven topic model and Wikipedia knowledge. With the supervision of Wikipedia knowledge, we could grasp more coherent aspects, namely, concepts, and detect the trends of concepts more accurately, the detected concept trends can reflect bursty content in text and people’s concern. Our method could detect events and discover events specific entities in text. Experiments on New York Times and TechCrunch datasets show that our framework outperforms two baselines.