Document Embedding using piped ELM-GAN Model

Document Embedding methods are an impressive task in each machine learning or neural network based natural language processing task. This task is entitled by representation learning and knowledge representation, too. In ultimate the target of this task, each document outputs a representation format of text documents in order to be understandable for machine. Literature reviews in representation learning, shows that document embedding methods for text is weaker in compare with representation of image or signal. Also, in compare to other data like as image or signal, representation of text has more challenges. By this, this paper we suggested a piped process of Generative Adversarial Neural Network and Extreme Learning Machine technique for document embedding. The experimental results show that document embedding using this combination of Generative Adversarial Networks and Extreme learning machines is comparative with other available methods of document embedding.