Automatic Persian Text Generation Using Rule-Based Models And Word Embedding

Natural language generation systems are the subset of natural language processing, have been around for a long time, but their technology as a commercial tool has recently become widespread. In natural language generation, the system needs to decide about how to put a concept among words. The ability for generating a meaningful text plays a key role in many natural language processing applications. The aim of this paper is to propose a method for generating text using artificial intelligence methods with the correct structure, a starting point for generating Persian(Farsi) texts. In order to promote the text generation, it has been attempted to use the combination of machine learning methods and probabilistic models. In the proposed model, the probabilistic models and Word2vec, as a word embedding method, are used to extract the rules and to vectorize the text, respectively. Then, combinating these and the cosine distance are used in the generation phase. The results indicate the performance of proposed model and the generated text has the appropriate structure, concept and variety. Also, the model is optimal in terms of humanity and complexity rather than other methods.