Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks.
Anna PotapenkoArtem PopovKonstantin V. VorontsovPublished in: CoRR (2017)
Keyphrases
- topic models
- neural network
- generative model
- probabilistic model
- latent dirichlet allocation
- topic modeling
- probabilistic topic models
- text documents
- generative process
- pattern recognition
- latent topics
- latent variables
- gibbs sampling
- low dimensional
- text mining
- variational inference
- graphical models
- vector space
- probabilistic latent semantic analysis
- monolingual and cross lingual
- latent topic models
- text corpora
- relevance model
- posterior probability
- data analysis
- bayesian networks
- dimensionality reduction
- co occurrence
- feature extraction
- hierarchical bayesian model
- language modeling framework
- information retrieval
- machine learning