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Found 6 papers, 2 papers with code
Probabilistic Topic Modelling with Transformer Representations
With the rise of transformers in Natural Language Processing, however, several successful models that rely on straightforward clustering approaches in transformer-based embedding spaces have emerged and consolidated the notion of topics as clusters of embedding vectors.
GPTopic: Dynamic and Interactive Topic Representations
Topic modeling seems to be almost synonymous with generating lists of top words to represent topics within large text corpora.
Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class
Few-shot methods for accurate modeling under sparse label-settings have improved significantly.
Twitmo: A Twitter Data Topic Modeling and Visualization Package for R
We present Twitmo, a package that provides a broad range of methods to collect, pre-process, analyze and visualize geo-tagged Twitter data.
Community-Detection via Hashtag-Graphs for Semi-Supervised NMF Topic Models
Extracting topics from large collections of unstructured text-documents has become a central task in current NLP applications and algorithms like NMF, LDA as well as their generalizations are the well-established current state of the art.
Coherence-Based Document Clustering
While these algorithms differ in their modeling approach, they have in common that hyperparameter optimization is difficult and is mainly achieved by maximizing the extracted topic coherence scores via a grid search.
