We are not able to deal with a mammoth text corpus without summarizing them into a
relatively small subset. A computational tool is extremely needed to understand such a …

We present a novel hierarchical distance-dependent Bayesian model for event coreference
resolution. While existing generative models for event coreference resolution are completely …

Topic models such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet processes
(HDP) are simple solutions to discover topics from a set of unannotated documents. While …

This study proposes a novel multi-label music emotion recognition (MER) system. An
emotion cannot be defined clearly in the real world because the classes of emotions are …

A latent Dirichlet allocation (LDA) model is a common method for mining the interest of
microblog users. But the LDA model does not reflect the hierarchical and dynamic trend of …

Abstract Electronic Medical Record (EMR) has established itself as a valuable resource for
large scale analysis of health data. A hospital EMR dataset typically consists of medical …

The objective of this paper is to propose a hierarchical topic evolution model (HTEM) that
can organize time‐varying topics in a hierarchy and discover their evolutions with multiple …

The distance dependent Chinese restaurant process (ddCRP) provides a flexible framework
for clustering data with temporal, spatial, or other structured dependencies. Here we model …

Side information, or auxiliary information associated with documents or image content,
provides hints for clustering. We propose a new model, side information dependent Chinese …

News events and social media are composed of evolving storylines, which capture public
attention for a limited period of time. Identifying storylines requires integrating temporal and …