26  Word Embeddings

26.1 Objectives

• Understanding what are word embeddings
• Methods: word2vec, doc2vec, GLoVe, SVD/LSA
• Constructing a feature-co-occurrence matrix
• Fitting a word embedding model
• Using pre-trained embedding scores with documents
• Prediction based on embeddings
• Further methods

26.2 Methods

Applicable methods for the objectives listed above.

26.3 Examples

Examples here.

26.4 Issues

Issues here.

26.5 Further Reading

Additional resources from libraries or the web.

26.6 Exercises

Add some here.