Interpretable probabilistic embeddings: bridging the gap between topic\n models and neural networks
2017PreprintPotapenko, Anna, Popov, Artem, Vorontsov, Konstantin
arXiv (Cornell University)
We consider probabilistic topic models and more recent word embedding\ntechniques from a perspective of learning hidden semantic representations.\nInspired by a striking similarity of the two approaches, we merge them and\nlearn probabilistic embeddings with online EM-algorithm on word co-occurrence\ndata. The resulting embeddings perform on par with Skip-Gram Negative Sampling\n(SGNS) on word similarity tasks and benefit in the interpretability of the\ncomponents. Next, we learn probabilistic document embeddings that outperform\nparagraph2vec on a document similarity task and require less memory and time\nfor training. Finally, we employ multimodal Additive Regularization of Topic\nModels (ARTM) to obtain a high sparsity and learn embeddings for other\nmodalities, such as timestamps and categories. We observe further improvement\nof word similarity performance and meaningful inter-modality similarities.\n