Topic modeling is a popular technique for exploring large document collections. It has
proven useful for this task, but its application poses a number of challenges. First, the …

When developing topic models, a critical question that should be asked is: How well will this
model work in an applied setting? Because standard performance evaluation of topic …

There is a rising need for automated analysis of news text, and topic models have proven to
be useful tools for this task. However, as the quality of the topics induced by topic models …

The recent explosion in work on neural topic modeling has been criticized for optimizing
automated topic evaluation metrics at the expense of actual meaningful topic identification …

Topic models are widely used unsupervised models capable of learning topics–weighted
lists of words and documents–from large collections of text documents. When topic models …

Probabilistic topic models have become one of the most widespread machine learning
techniques in textual analysis. Topic discovering is an unsupervised process that does not …

Consider a continuous word embedding model. Usually, the cosines between word vectors
are used as a measure of similarity of words. These cosines do not change under …

Artificial Intelligence (AI) and related fields, based on decades of research, have seen a rise
in recent years and are expected to have a significant impact on large parts of industry and …

The amount of text data is increasing, and the development of hardware and software
platforms for the web and social networks enabled the rapid creation of large repositories …

Artificial Intelligence (AI) and related fields, based on decades of research, have seen a rise
in recent years and are expected to have a significant impact on large parts of industry and …