Search Results for author:
Found 3 papers, 1 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.
Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence
We demonstrate the competitive performance of our method with a large benchmark study, and achieve superior results compared to state-of-the-art topic modeling and document clustering models.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
