Probabilistic Topic Modeling With Transformer Representations
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Summary
This summary is machine-generated.We introduce the Transformer-Representation Neural Topic Model (TNTM), a novel approach combining transformer embeddings with probabilistic topic modeling. This method enhances topic coherence and diversity for natural language processing tasks.
Area Of Science
- Natural Language Processing (NLP)
- Machine Learning
- Artificial Intelligence
Background
- Topic modeling traditionally relied on Bayesian graphical models.
- Recent advances leverage transformer embeddings for topic discovery.
- Existing transformer-based methods often use simple clustering, lacking probabilistic depth.
Purpose Of The Study
- To propose the Transformer-Representation Neural Topic Model (TNTM).
- To integrate transformer-based embeddings with probabilistic topic modeling.
- To enhance topic coherence and diversity in NLP.
Main Methods
- Developed TNTM, unifying transformer embeddings with probabilistic modeling.
- Utilized the Variational Autoencoder (VAE) framework for efficient inference.
- Leveraged transformer-based embedding spaces for topic representation.
Main Results
- TNTM achieves state-of-the-art performance in embedding coherence.
- The model maintains high topic diversity.
- Experimental results demonstrate competitive performance against existing methods.
Conclusions
- TNTM offers a powerful hybrid approach to topic modeling.
- The model successfully combines the strengths of transformer embeddings and probabilistic methods.
- TNTM provides a flexible and effective tool for uncovering latent topics in text data.
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