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Vacuum circuit breakers are widely used in distribution power
systems for their advantages such as maintenance free and
eco-friendly. Nowadays, most circuit breakers used at transmission
voltage level are SF6 circuit breakers, but the SF6 they emit is
one of the six greenhouse gases defined in Kyoto Protocol.
Therefore, the development of transmission voltage level vacuum
circuit breaker can help the environment. The switching arc
phenomena in transmission voltage level vacuum circuit breakers are
key issues to explore. This book focuses on the high-current vacuum
arcs phenomena at transmission voltage level, especially on the
anode spot phenomena, which significantly influence the success or
failure of the short circuit current interruption. Then, it
addresses the dielectric recovery property in current interruption.
Next it explains how to determine the closing/opening displacement
curve of transmission voltage level vacuum circuit breakers based
on the vacuum arc phenomena. After that, it explains how to
determine key design parameters for vacuum interrupters and vacuum
circuit breakers at transmission voltage level. At the end, the
most challenging issue for vacuum circuit breakers, capacitive
switching in vacuum, is addressed. The contents of this book will
benefit researchers and engineers in the field of power
engineering, especially in the field of power circuit breakers and
power switching technology.
Vacuum circuit breakers are widely used in distribution power
systems for their advantages such as maintenance free and
eco-friendly. Nowadays, most circuit breakers used at transmission
voltage level are SF6 circuit breakers, but the SF6 they emit is
one of the six greenhouse gases defined in Kyoto Protocol.
Therefore, the development of transmission voltage level vacuum
circuit breaker can help the environment. The switching arc
phenomena in transmission voltage level vacuum circuit breakers are
key issues to explore. This book focuses on the high-current vacuum
arcs phenomena at transmission voltage level, especially on the
anode spot phenomena, which significantly influence the success or
failure of the short circuit current interruption. Then, it
addresses the dielectric recovery property in current interruption.
Next it explains how to determine the closing/opening displacement
curve of transmission voltage level vacuum circuit breakers based
on the vacuum arc phenomena. After that, it explains how to
determine key design parameters for vacuum interrupters and vacuum
circuit breakers at transmission voltage level. At the end, the
most challenging issue for vacuum circuit breakers, capacitive
switching in vacuum, is addressed. The contents of this book will
benefit researchers and engineers in the field of power
engineering, especially in the field of power circuit breakers and
power switching technology.
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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 17th China National Conference, CCL 2018, and 6th International Symposium, NLP-NABD 2018, Changsha, China, October 19-21, 2018, Proceedings (Paperback, 1st ed. 2018)
Maosong Sun, Ting Liu, Xiaojie Wang, Zhiyuan Liu, Yang Liu
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R1,599
Discovery Miles 15 990
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Ships in 10 - 15 working days
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This book constitutes the proceedings of the 17th China National
Conference on Computational Linguistics, CCL 2018, and the 6th
International Symposium on Natural Language Processing Based on
Naturally Annotated Big Data, NLP-NABD 2018, held in Changsha,
China, in October 2018. The 33 full papers presented in this volume
were carefully reviewed and selected from 84 submissions. They are
organized in topical sections named: Semantics; machine
translation; knowledge graph and information extraction; linguistic
resource annotation and evaluation; information retrieval and
question answering; text classification and summarization; social
computing and sentiment analysis; and NLP applications.
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, social
network 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.
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, social
network 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.
heterogeneous graphs. Further, the book introduces different
applications of NE such as recommendation and information diffusion
prediction. Finally, the book concludes the methods and
applications and looks forward to the future directions.
This open access book provides an overview of the recent advances
in representation learning theory, algorithms and applications for
natural language processing (NLP). It is divided into three parts.
Part I presents the representation learning techniques for multiple
language entries, including words, phrases, sentences and
documents. Part II then introduces the representation techniques
for those objects that are closely related to NLP, including
entity-based world knowledge, sememe-based linguistic knowledge,
networks, and cross-modal entries. Lastly, Part III provides open
resource tools for representation learning techniques, and
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,
social network 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.
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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Chinese Computational Linguistics - 18th China National Conference, CCL 2019, Kunming, China, October 18-20, 2019, Proceedings (Paperback, 1st ed. 2019)
Maosong Sun, Xuanjing Huang, Heng Ji, Zhiyuan Liu, Yang Liu
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R1,696
Discovery Miles 16 960
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Ships in 10 - 15 working days
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This book constitutes the proceedings of the 18th China National
Conference on Computational Linguistics, CCL 2019, held in Kunming,
China, in October 2019. The 56 full papers presented in this volume
were carefully reviewed and selected from 134 submissions. They
were organized in topical sections named: linguistics and cognitive
science, fundamental theory and methods of computational
linguistics, information retrieval and question answering, text
classification and summarization, knowledge graph and information
extraction, machine translation and multilingual information
processing, minority language processing, language resource and
evaluation, social computing and sentiment analysis, NLP
applications.
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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 15th China National Conference, CCL 2016, and 4th International Symposium, NLP-NABD 2016, Yantai, China, October 15-16, 2016, Proceedings (Paperback, 1st ed. 2016)
Maosong Sun, Xuanjing Huang, Hongfei Lin, Zhiyuan Liu, Yang Liu
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R2,987
Discovery Miles 29 870
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Ships in 10 - 15 working days
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This book constitutes the proceedings of the 15th China National
Conference on Computational Linguistics, CCL 2016, and the 4th
International Symposium on Natural Language Processing Based on
Naturally Annotated Big Data, NLP-NABD 2016, held in Yantai City,
China, in October 2016. The 29 full papers and 8 short papers
presented in this volume were carefully reviewed and selected from
85 submissions. They were organized in topical sections named:
semantics; machine translation; multilinguality in NLP; knowledge
graph and information extraction; linguistic resource annotation
and evaluation; information retrieval and question answering; text
classification and summarization; social computing and sentiment
analysis; and NLP applications.
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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data - 14th China National Conference, CCL 2015 and Third International Symposium, NLP-NABD 2015, Guangzhou, China, November 13-14, 2015, Proceedings (Paperback, 1st ed. 2015)
Maosong Sun, Zhiyuan Liu, Min Zhang, Yang Liu
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R2,876
Discovery Miles 28 760
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 14th China
National Conference on Computational Linguistics, CCL 2014, and of
the Third International Symposium on Natural Language Processing
Based on Naturally Annotated Big Data, NLP-NABD 2015, held in
Guangzhou, China, in November 2015. The 34 papers presented were
carefully reviewed and selected from 283 submissions. The papers
are organized in topical sections on lexical semantics and
ontologies; semantics; sentiment analysis, opinion mining and text
classification; machine translation; multilinguality in NLP;
machine learning methods for NLP; knowledge graph and information
extraction; discourse, coreference and pragmatics; information
retrieval and question answering; social computing; NLP
applications.
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