lda2vec-tf
TensorFlow implementation of Christopher Moody's lda2vec, a hybrid of Latent Dirichlet Allocation & word2vec
The lda2vec model simultaneously learns embeddings (continuous dense vector representations) for:
- words (based on word and document context),
- topics (in the same latent word space), and
- documents (as sparse distributions over topics).
[ + integrated with the tf Embeddings Projector to interactively visualize results ]
WIP
Check back for updated docs and a walk-through example.
Meanwhile, read the paper and see the excellent README @ the original repo.
Requirements
- Python 3
- TensorFlow 0.12.0+
- numpy
- pandas