Co-occurrence based Word Embeddings
In co-occurrence based methods, word embeddings are typically learned from a unsupervised corpus, where the model is optimized for different tasks and then used the first layer of the word features as embedding. There are different methods for this embedding. In the co-occurrence based word embeddings, the main assumption is that co-occurring words are related to each other. In that way, the models learn a dense vector representation for each word.