Topic models have been prevalent for decades to discover latent topics and infer topic
proportions of documents in an unsupervised fashion. They have been widely used in …

Topic modelling has been a successful technique for text analysis for almost twenty years.
When topic modelling met deep neural networks, there emerged a new and increasingly …

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 …

Link prediction insimple graphs is a fundamental problem in which new links between
vertices are predicted based on the observed structure of the graph. However, in many real …

Text documents are usually connected in a graph structure, resulting in an important class of
data named text-attributed graph, eg, paper citation graph and Web page hyperlink graph …

Text classification is an important and classical problem in natural language processing.
Recently, Graph Neural Networks (GNNs) have been widely applied in text classification …

A huge volume of user-generated content is daily produced on social media. To facilitate
automatic language understanding, we study keyphrase prediction, distilling salient …

Due to the overload of published scientific articles, citation recommendation has long been a
critical research problem for automatically recommending the most relevant citations of …

Graph Neural Networks (GNNs) such as GCN can effectively learn document
representations via the semantic relation graph among documents and words. However …

Connectivity across documents often exhibits a hierarchical network structure. Hyperbolic
Graph Neural Networks (HGNNs) have shown promise in preserving network hierarchy …