Summary

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), they also give a richer feature representation than other 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 this problem to develop an understanding of 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.

Next, we explored neural-network-based word representation learning methods. First, we worked out an example by hand to understand how word embeddings or word vectors can be calculated to help us understand...