Topic modeling is used in information retrieval to infer the hidden themes in a collection of
documents and thus provides an automatic means to organize, understand and summarize …

The goal of text-to-text generation is to make machines express like a human in many
applications such as conversation, summarization, and translation. It is one of the most …

Topic models can be useful tools to discover latent topics in collections of documents.
Recent studies have shown the feasibility of approach topic modeling as a clustering task …

Analyzing short texts infers discriminative and coherent latent topics that is a critical and
fundamental task since many real-world applications require semantic understanding of …

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 …

Background Automatic clinical text classification is a natural language processing (NLP)
technology that unlocks information embedded in clinical narratives. Machine learning …

Automatic text classification is the task of organizing documents into pre-determined classes,
generally using machine learning algorithms. Generally speaking, it is one of the most …

Probabilistic topic models are widely used to discover latent topics in document collections,
while latent feature vector representations of words have been used to obtain high …

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 …

With the massive growth of the Internet, text data has become one of the main formats of
tourism big data. As an effective expression means of tourists' opinions, text mining of such …