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Representation Learning for Natural Language Processing
Published by Springer (edition 2nd ed. 2023), 2023
ISBN 10: 9819915996 ISBN 13: 9789819915996
Language: English
Hardcover. Condition: New. 2nd ed. 2023. The item is brand new, never used or read. It's in perfect condition and may include supplements and/or access codes or come shrink-wrapped.
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Representation Learning for Natural Language Processing
Seller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 5 out of 5 stars
hardcover. Condition: Very Good.
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Representation Learning for Natural Language Processing
Seller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 5 out of 5 stars
hardcover. Condition: Good. Book is bent.
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Paperback. Condition: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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paperback. Condition: Very Good. Crease on cover and a few pages*.
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Condition: As New. Unread book in perfect condition.
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Representation Learning for Natural Language Processing
Published by Springer (edition 1st ed. 2020), 2020
ISBN 10: 9811555729 ISBN 13: 9789811555725
Language: English
Hardcover. Condition: Very Good. 1st ed. 2020. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Condition: New.
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Introduction to Graph Neural Networks
Published by Springer International Publishing AG, CH, 2020
ISBN 10: 3031004590 ISBN 13: 9783031004599
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: New. Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool.This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.
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Introduction to Graph Neural Networks (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In English.
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Representation Learning for Natural Language Processing (Paperback)
Published by Springer Verlag, Singapore, Singapore, 2023
ISBN 10: 981991602X ISBN 13: 9789819916023
Language: English
Paperback. Condition: new. Paperback. This book provides an overview of the recent advances in representation learning theory, algorithms, and applications for natural language processing (NLP), ranging from word embeddings to pre-trained language models. It is divided into four parts. Part I presents the representation learning techniques for multiple language entries, including words, sentences and documents, as well as pre-training techniques. Part II then introduces the related representation techniques to NLP, including graphs, cross-modal entries, and robustness. Part III then introduces the representation techniques for the knowledge that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, legal domain knowledge and biomedical domain knowledge. Lastly, Part IV discusses the remaining challenges and future research directions.The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, socialnetwork analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.As compared to the first edition, the second edition (1) provides a more detailed introduction to representation learning in Chapter 1; (2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning; (3) updates recent advances in representation learning in all chapters; and (4) corrects some errors in the first edition. The new contents will be approximately 50%+ compared to the first edition. This is an open access book. (2) adds four new chapters to introduce pre-trained language models, robust representation learning, legal knowledge representation learning and biomedical knowledge representation learning; Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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PF. Condition: New.
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Condition: New.
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Condition: New.
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Condition: As New. Unread book in perfect condition.
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Introduction to Graph Neural Networks
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Condition: New.
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Introduction to Graph Neural Networks
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Representation Learning for Natural Language Processing
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Representation Learning for Natural Language Processing
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Hardcover. Condition: Near Fine. Dust Jacket Condition: Very Good. First Edition. Some chips on jacket extremities, light soil ; 8vo 8" - 9" tall.
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Representation Learning for Natural Language Processing
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
