In this paper, we investigate the multimodal nature of cell phone data in terms of discovering recurrent and rich patterns in people’s lives. We present a method that can discov...
In this paper, we investigate how modeling content structure can benefit text analysis applications such as extractive summarization and sentiment analysis. This follows the lingu...
This paper introduces LDA-G, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (suc...
Identifying the most influential documents in a corpus is an important problem in many fields, from information science and historiography to text summarization and news aggregati...
We propose a new algorithm for dimensionality reduction and unsupervised text classification. We use mixture models as underlying process of generating corpus and utilize a novel,...