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Embeddings in natural language processing : = theory and advances in vector representations of meaning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Embeddings in natural language processing :/ Mohammad Taher Pilehvar, Jose Camacho-Collados.
其他題名:	theory and advances in vector representations of meaning /
作者:	Pilehvar, Mohammad Taher.
其他作者:	Camacho-Collados, Jose.
出版者:	[San Rafael, Calif.] :Morgan & Claypool, : c2021.,
面頁冊數:	xvii, 157 p. :ill. ; : 24 cm.;
標題:	Natural language processing (Computer science) -
ISBN:	9781636390215 (pbk.) :

Embeddings in natural language processing : = theory and advances in vector representations of meaning /
Pilehvar, Mohammad Taher.

Embeddings in natural language processing :theory and advances in vector representations of meaning /Mohammad Taher Pilehvar, Jose Camacho-Collados. - [San Rafael, Calif.] :Morgan & Claypool,c2021. - xvii, 157 p. :ill. ;24 cm. - Synthesis lectures on human language technologies,#471947-4040 ;. - Synthesis lectures on human language technologies ;#18. .

Includes bibliographical references (p. 111-155).

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.

ISBN: 9781636390215 (pbk.) :NT1947Subjects--Topical Terms:

641811
Natural language processing (Computer science)

LC Class. No.: QA76.9.N38 / P554 2021

Dewey Class. No.: 006.3/5

Embeddings in natural language processing : = theory and advances in vector representations of meaning /
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520 $a 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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