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Found 7 papers, 3 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.
Structural Neural Additive Models: Enhanced Interpretable Machine Learning
Deep neural networks (DNNs) have shown exceptional performances in a wide range of tasks and have become the go-to method for problems requiring high-level predictive power.
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
We propose Neural Additive Models for Location Scale and Shape (NAMLSS), a modelling framework that combines the predictive power of classical deep learning models with the inherent advantages of distributional regression while maintaining the interpretability of additive models.
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.
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.
