While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in
topic modeling, they often require detailed assumptions and careful specification of …

Each day humans generate massive volumes of data in a variety of different forms (Lazer et
al., 2009). For example, digitized texts provide a rich source of political content through …

Multilingual topic models can reveal patterns in cross-lingual document collections.
However, existing models lack speed and interactivity, which prevents adoption in everyday …

Interactive topic models are powerful tools for those seeking to understand large collections
of text. However, existing sampling-based interactive topic modeling approaches scale …

Today, we have both the blessing and the curse of being overloaded with information. Never
before has text been more important to how we communicate, or more easily available. But …

Topic models provide insights into document collections, and their supervised extensions
also capture associated document-level metadata such as sentiment. However, inferring …

Many predictive tasks, such as diagnosing a patient based on their medical chart, are
ultimately defined by the decisions of human experts. Unfortunately, encoding experts' …

Topic modeling is widely used to uncover the latent thematic structure in corpora. Based on
the separability assumption, the spectral method focuses on the word co-occurrence …

Spectral inference provides fast algorithms and provable optimality for latent topic analysis.
But for real data these algorithms require additional ad-hoc heuristics, and even then often …

Hierarchical topic models nowadays tend to capture the relationship between words and
topics, often ignoring the role of anchor words that guide text generation. For the first time …