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Embeddings in Natural Language Processing

Theory and Advances in Vector Representations of Meaning

By: Mohammad Taher Pilehvar,Jose Camacho-Collados

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Embeddings in Natural Language Processing

Theory and Advances in Vector Representations of Meaning

By: Mohammad Taher Pilehvar,Jose Camacho-Collados

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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Page Count

157

Publisher

Springer Nature

Published Date

ISBN 10

3031021770

ISBN 13

9783031021770

Embeddings in Natural Language Processing

Theory and Advances in Vector Representations of Meaning

By: Mohammad Taher Pilehvar,Jose Camacho-Collados

Write a review Read reviews Take a Quiz Solve Book Puzzle

Embeddings in Natural Language Processing

Theory and Advances in Vector Representations of Meaning

By: Mohammad Taher Pilehvar,Jose Camacho-Collados

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.

Average ratings

N/A

5

0%

4

0%

3

0%

2

0%

1

0%

Solve jigsaw puzzle

Age

Page Count

157

Publisher

Springer Nature

Published Date

ISBN 10

3031021770

ISBN 13

9783031021770