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Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings.