The evolution of topic modeling
R Churchill, L Singh - ACM Computing Surveys, 2022 - dl.acm.org
Topic models have been applied to everything from books to newspapers to social media
posts in an effort to identify the most prevalent themes of a text corpus. We provide an in …
posts in an effort to identify the most prevalent themes of a text corpus. We provide an in …
Topic modeling using latent Dirichlet allocation: A survey
We are not able to deal with a mammoth text corpus without summarizing them into a
relatively small subset. A computational tool is extremely needed to understand such a …
relatively small subset. A computational tool is extremely needed to understand such a …
Topic modeling in embedding spaces
Topic modeling analyzes documents to learn meaningful patterns of words. However,
existing topic models fail to learn interpretable topics when working with large and heavy …
existing topic models fail to learn interpretable topics when working with large and heavy …
Topic discovery via latent space clustering of pretrained language model representations
Topic models have been the prominent tools for automatic topic discovery from text corpora.
Despite their effectiveness, topic models suffer from several limitations including the inability …
Despite their effectiveness, topic models suffer from several limitations including the inability …
The dynamic embedded topic model
Topic modeling analyzes documents to learn meaningful patterns of words. For documents
collected in sequence, dynamic topic models capture how these patterns vary over time. We …
collected in sequence, dynamic topic models capture how these patterns vary over time. We …
Investigating the efficient use of word embedding with neural-topic models for interpretable topics from short texts
R Murakami, B Chakraborty - Sensors, 2022 - mdpi.com
With the rapid proliferation of social networking sites (SNS), automatic topic extraction from
various text messages posted on SNS are becoming an important source of information for …
various text messages posted on SNS are becoming an important source of information for …
Neural variational sparse topic model for sparse explainable text representation
Texts are the major information carrier for internet users, from which learning the latent
representations has important research and practical value. Neural topic models have been …
representations has important research and practical value. Neural topic models have been …
Large scale subject category classification of scholarly papers with Deep Attentive Neural Networks
Subject categories of scholarly papers generally refer to the knowledge domain (s) to which
the papers belong, examples being computer science or physics. Subject category …
the papers belong, examples being computer science or physics. Subject category …
textprep: A text preprocessing toolkit for topic modeling on social media data [textprep: A text preprocessing toolkit for topic modeling on social media data]
R Churchill, L Singh - Proceedings of the 10th International Conference …, 2021 - par.nsf.gov
With the rapid growth of social media in recent years, there has been considerable effort
toward understanding the topics of online discussions. Unfortunately, state of the art topic …
toward understanding the topics of online discussions. Unfortunately, state of the art topic …
A decision support system in precision medicine: contrastive multimodal learning for patient stratification
Precision medicine aims to provide personalized healthcare for patients by stratifying them
into subgroups based on their health conditions, enabling the development of tailored …
into subgroups based on their health conditions, enabling the development of tailored …