Topic modeling is an unsupervised learning task that discovers the hidden topics in a
collection of documents. In turn, the discovered topics can be used for summarizing …

Topic models have been widely used to infer latent topics in text documents. However, the
unsupervised topic models often result in incoherent topics, which always confused users in …

Topic modeling has been widely used in text mining. Previous topic models such as Latent
Dirichlet Allocation (LDA) are successful in learning hidden topics but they do not take into …

Topic models are unsupervised machine learning techniques that output clusters of “topics”
represented as co-occurring words with their associated probability distributions. Topic …

Many applications require semantic understanding of short texts, and inferring discriminative
and coherent latent topics is a critical and fundamental task in these applications …

Topic models have been widely used for text analysis. Previous topic models have enjoyed
great success in mining the latent topic structure of text documents. With many efforts made …

Probabilistic topic models have been extensively used to extract low-dimension aspects
from document collections. However, such models without any human knowledge often …

For many applications that require semantic understanding of short texts, inferring
discriminative and coherent latent topics from short texts is a critical and fundamental task …

Word embedding maps words into a low-dimensional continuous embedding space by
exploiting the local word collocation patterns in a small context window. On the other hand …

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 …