Topic modeling is a well-established technique for exploring text corpora. Conventional
topic models (eg, LDA) represent topics as bags of words that often require" reading the tea …

Current text classification methods typically require a good number of human-labeled
documents as training data, which can be costly and difficult to obtain in real applications …

Topic models have been prevalent for decades with various applications. However, existing
topic models commonly suffer from the notorious topic collapsing: discovered topics …

Topic models have been the prominent tools for automatic topic discovery from text corpora.
Despite their effectiveness, topic models suffer from several limitations including the inability …

In this paper, we explore text classification with extremely weak supervision, ie, only relying
on the surface text of class names. This is a more challenging setting than the seed-driven …

Unsupervised clustering is widely used to explore large corpora, but existing formulations
neither consider the users' goals nor explain clusters' meanings. We propose a new task …

Mining a set of meaningful topics organized into a hierarchy is intuitively appealing since
topic correlations are ubiquitous in massive text corpora. To account for potential …

Training deep neural networks (DNNs) with limited supervision has been a popular research
topic as it can significantly alleviate the annotation burden. Self-training has been …

We created this CORD-NER dataset with comprehensive named entity recognition (NER) on
the COVID-19 Open Research Dataset Challenge (CORD-19) corpus (2020-03-13). This …

Recent work has demonstrated that pre-trained language models (PLMs) are zero-shot
learners. However, most existing zero-shot methods involve heavy human engineering or …