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Embeddings in Natural Language Processing [electronic resource] : Theory and Advances in Vector Representations of Meaning / by Mohammad Taher Pilehvar, Jose Camacho-Collados.

By: Pilehvar, Mohammad Taher [author.].
Contributor(s): Camacho-Collados, Jose [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Human Language Technologies: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2021Edition: 1st ed. 2021.Description: XVIII, 157 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031021770.Subject(s): Artificial intelligence | Natural language processing (Computer science) | Computational linguistics | Artificial Intelligence | Natural Language Processing (NLP) | Computational LinguisticsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Preface -- Introduction -- Background -- Word Embeddings -- Graph Embeddings -- Sense Embeddings -- Contextualized Embeddings -- Sentence and Document Embeddings -- Ethics and Bias -- Conclusions -- Bibliography -- Authors' Biographies.
In: Springer Nature eBookSummary: 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. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. 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. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, 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.
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Preface -- Introduction -- Background -- Word Embeddings -- Graph Embeddings -- Sense Embeddings -- Contextualized Embeddings -- Sentence and Document Embeddings -- Ethics and Bias -- Conclusions -- Bibliography -- Authors' Biographies.

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. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. 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. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, 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.

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