Generative Adversarial Networks

Recently I have been drawn to generative models, such as LDA (latent Dirichlet allocation) and other topic models. In deep learning, there are a few examples, such as FVBN (fully visible belief networks), VAE (variational autoencoder), RBM (restricted Boltzmann machine) etc. Recently I have been reading about GAN (generative adversarial networks), first published by Ian Goodfellow … More Generative Adversarial Networks

Python Package for Short Text Mining

There has been a lot of methods for natural language processing and text mining. However, in tweets, surveys, Facebook, or many online data, texts are short, lacking data to build enough information. Traditional bag-of-words (BOW) model gives sparse vector representation. Semantic relations between words are important, because we usually do not have enough data to … More Python Package for Short Text Mining

LDA2Vec: a hybrid of LDA and Word2Vec

Both LDA (latent Dirichlet allocation) and Word2Vec are two important algorithms in natural language processing (NLP). LDA is a widely used topic modeling algorithm, which seeks to find the topic distribution in a corpus, and the corresponding word distributions within each topic, with a prior Dirichlet distribution. Word2Vec is a vector-representation model, trained from RNN (recurrent … More LDA2Vec: a hybrid of LDA and Word2Vec