- NVDM from https://arxiv.org/pdf/1511.06038.pdf -- ICML 2016
- GSM from https://arxiv.org/pdf/1706.00359.pdf -- ICML 2017
- NVLDA from https://arxiv.org/pdf/1703.01488.pdf -- ICLR 2017
- ProdLDA from https://arxiv.org/pdf/1703.01488.pdf -- ICLR 2017
- NSMTM from https://arxiv.org/pdf/1810.09079.pdf -- WSDM 2019
- NSMDM from https://arxiv.org/pdf/1810.09079.pdf -- WSDM 2019
- Scholar from https://arxiv.org/abs/1705.09296 -- ACL 2018
- NVCTM from https://dl.acm.org/doi/10.1145/3308558.3313561 -- WWW 2019
- online-LDA: LDA using online variational inference
- online-LDA: LDA using Gibbs sampling
- NMF: online NMF
- Perplexity of unseen documents: All models, except LDA_gibbs and NMF
- Perplexity of unseen/held-out words: All models, except NMF
- Topic coherence: All models
- Performance in document classification: All models
- [1] Title of news articles in W2E dataset from https://dl.acm.org/doi/abs/10.1145/3269206.3269309
- [2] Web snippets from https://papers.nips.cc/paper/2002/hash/3147da8ab4a0437c15ef51a5cc7f2dc4-Abstract.html
- [1] Content of news articles in W2E dataset from https://dl.acm.org/doi/abs/10.1145/3269206.3269309
- [2] 20News
The datasets are preprocessed and shared here. Please download and unzip the files into the preprocessed_data folder
- train/trainer_neural_topic_model.py: for neural models
- train/trainer_lda_topic_model.py: for LDA models
- train/trainer_nmf_topic_model.py: for NMF models
- evaluation/eval_neural_models.py: for neural models
- evaluation/eval_lda_models.py: for LDA models
- evaluation/eval_nmf_models.py: for NMF models
- empirical_studies/examine_models.py