Topic models are a popular way to uncover the topics that are assumed to have generated the documents of a collection. Latent Semantic Analysis (LSA) uses singular value decomposition to approximate the term-document and it is discussed to be a computational model that implements the distributional hypothesis (you should know a word by the company it keeps). The different models to derive word embeddings also belong at this category.Could we say the same for Latent Dirichlet Allocation (LDA) that has a complete generative story and clear probabilistic undergrounds? Are you aware of any resources that cover this? 
Topic models and distributional hypothesis
machine learning;word embeddings;topic model;theory