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 develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling.
The nHDP generalizes the nested Chinese restaurant process (nCRP) to allow each word to …

Hierarchical topic modeling aims to discover latent topics from a corpus and organize them
into a hierarchy to understand documents with desirable semantic granularity. However …

To help users quickly understand the major opinions from massive online reviews, it is
important to automatically reveal the latent structure of the aspects, sentiment polarities, and …

Hierarchical topic models, which can extract semantically meaningful topics from a
textcorpus in an unsupervised manner and automatically organise them into a topic …

This paper presents a tree-structured neural topic model, which has a topic distribution over
a tree with an infinite number of branches. Our model parameterizes an unbounded …

Recently, it has been found that e-commerce (EC) websites provide a large amount of useful
information that exceed the human cognitive processing capacity. In order to help customers …

This paper presents a novel unsupervised abstractive summarization method for
opinionated texts. While the basic variational autoencoder-based models assume a …

Inspired by a two-level theory that unifies agenda setting and ideological framing, we
propose supervised hierarchical latent Dirichlet allocation (SHLDA) which jointly captures …

Keyphrases can concisely describe the high-level topics discussed in a document that
usually possesses hierarchical topic structures. Thus, it is crucial to understand the …