Word embeddings have become an integral part of many NLP tasks and are widely used for tasks such as machine translation, chatbots, image caption generation, and language modeling. Not only do word embeddings act as a dimensionality reduction technique (compared to one-hot encoding) but they also give a richer feature representation than other existing techniques. In this chapter, we discussed two popular neural network-based methods for learning word representations, namely the skip-gram model and the CBOW model.
First, we discussed the classical approaches to develop an understanding about how word representations were learned in the past. We discussed various methods such as using WordNet, building a co-occurrence matrix of the words, and calculating TF-IDF. Later, we discussed the limitations of these approaches.
This motivated us to explore neural network-based word representation learning methods. First, we worked out an example by hand to understand how word embeddings or word...
