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Representation Learning for Natural Language Processing
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.
Dictionary Catalog
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Machine Learning, Optimization, and Data Science
This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.
Catalogues of Sale
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User Modeling 2001
This book constitutes the refereed proceedings of the 8th International Conference on User Modeling, UM 2001, held in Sonthofen, Germany in July 2001. The 19 revised full papers and 20 poster summaries presented together with summaries of 12 selected student presentations were carefully reviewed and selected from 79 submissions. The book offers topical sections on acquiring user models from multi-modal user input; learning interaction models; user models for natural language interpretation, processing, and generation; adaptive interviewing for acquiring user preferences and product customization; supporting user collaboration through adaptive agents; student modeling; and adaptive information filtering, retrieval, and browsing.
The Diatom World
Diatom biology, genomics and ecology are becoming more relevant to the human species. While there have been recent compilations of some of the applied aspects of diatoms, and the dizzying pace of taxonomic revisions, this new volume bring us up to date on their classification, biology and ecology, as well as covering the topics of genomics and applied uses. In this collection, some of the leaders in diatom research present either new information or summarize recent research efforts on a wide range of topics, including the tree of life of diatoms, their classifications, the wide habitats and ecological spectra the group exploits, as well as the beauty of their form. This volume celebrates the diversity, emerging areas of research and fascinating ecology of the diatoms bringing this group of world-renown and emerging research leaders together. 'The Diatom World' will foster greater appreciation and research contributions on this incredibly diverse and fascinating group of organisms.
Tumor Suppressor Genes in Human Cancer
David Fisher, MD, PhD, and an authoritative panel of academic, cutting-edge researchers review and summarize the current state of the field. Describing the broad roles of tumor suppressors from a perspective based in molecular biology and genetics, the authors detail the major suppressors and the pathways they regulate, including cell cycle progression, stress responses, apoptosis, and responses to DNA damage. Leading-edge and forward-looking, Tumor Suppressor Genes in Human Cancer illuminates what is currently known of tumor suppressor genes and their regulation, work that is already beginning to revolutionize cancer target elucidation, drug discovery, and treatment design.
