Unsupervised Feature-Rich Clustering

Unsupervised clustering of documents is challenging because documents can conceivably be divided across multiple dimensions. Motivated by prior work incorporating expressive features into unsupervised generative models, this paper presents an unsupervised model for categorizing textual data which is capable of utilizing arbitrary features over a large context. Utilizing locally normalized log-linear models in the generative process, we offer straightforward extensions to the standard multinomial mixture model that allow us to effectively utilize automatically derived complex linguistic, statistical, and metadata features to influence the learned cluster structure for the desired task. We extensively evaluate and analyze the model’s capabilities over four distinct clustering tasks: topic, perspective, sentiment analysis, and Congressional bill survival, and show that this model outperforms strong baselines and state-of-the-art models.