Embeddings in Natural Language Processing : Theory and Advances in Vector Representations of Meaning, Paperback by Pilehvar, Mohammad Taher; Camacho-collados, Jose, ISBN 3031010493, ISBN-13 9783031010491, Like New Used, Free shipping in the US

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 book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. Th starts by explaining conventional word vector space models and word embeddings (., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. Th also provides an overview of recent developments in contextualized representations (., ELMo and BERT) and explains their potential in th, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.