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
Автор: Mohammad Taher Pilehvar, Jose Camacho-Collados
Издательство: Morgan & Claypool
Год: 2021
Страниц: 175
Язык: английский
Формат: pdf (true)
Размер: 11.7 MB
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 as sentences and documents.