Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP).
Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP.
Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such a.
Morgan & Claypool Embeddings in natural language processing: theory and advances in vector representations of meaning, paperback/mohammad taher pilehvar
