Many classification models work poorly on short texts due to data sparsity. To address this
issue, we propose topic memory networks for short text classification with a novel topic …

Topic models have been prevailing for many years on discovering latent semantics while
modeling long documents. However, for short texts they generally suffer from data sparsity …

In recent years, advances in neural variational inference have achieved many successes in
text processing. Examples include neural topic models which are typically built upon …

In this paper, we introduce a novel BERT-enhanced Relational Sentence Ordering Network
(referred to as BRSON) by leveraging BERT for capturing better dependency relationship …

Increasingly taking place in online spaces, modern political conversations are typically
perceived to be unproductively affirming---siloed in so called" echo chambers" of exclusively …

This paper presents an unsupervised framework for jointly modeling topic content and
discourse behavior in microblog conversations. Concretely, we propose a neural model to …

This paper studies automatic keyphrase extraction on social media. Previous works have
achieved promising results on it, but they neglect human reading behavior during keyphrase …

Quotations are crucial for successful explanations and persuasions in interpersonal
communications. However, finding what to quote in a conversation is challenging for …

Since effective semantic representations are utilized in many practical applications, inferring
discriminative and coherent latent topics from short texts is a critical and basic task …

Online healthcare communities (OHCs) have become producers of medical information.
Solving the issue of how to effectively reuse such a large amount of medical data and …