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1. |
Record Nr. |
UNISA996465462803316 |
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Autore |
Liu Zhiyuan |
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Titolo |
Representation Learning for Natural Language Processing [[electronic resource] /] / by Zhiyuan Liu, Yankai Lin, Maosong Sun |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XXIV, 334 p. 131 illus., 99 illus. in color.) |
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Disciplina |
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Soggetti |
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Natural language processing (Computer science) |
Computational linguistics |
Artificial intelligence |
Data mining |
Natural Language Processing (NLP) |
Computational Linguistics |
Artificial Intelligence |
Data Mining and Knowledge Discovery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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1. Representation Learning and NLP -- 2. Word Representation -- 3. Compositional Semantics -- 4. Sentence Representation -- 5. Document Representation -- 6. Sememe Knowledge Representation -- 7. World Knowledge Representation -- 8. Network Representation -- 9. Cross-Modal Representation -- 10. Resources -- 11. Outlook. |
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Sommario/riassunto |
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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 |
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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. |
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2. |
Record Nr. |
UNINA9910557669503321 |
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Autore |
Garmyn Andrea |
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Titolo |
Consumer Preferences and Acceptance of Meat Products |
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Pubbl/distr/stampa |
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Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
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Descrizione fisica |
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1 online resource (222 p.) |
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Soggetti |
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Biology, life sciences |
Food & society |
Research & information: general |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Sommario/riassunto |
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This Special Issue "Consumer Preferences and Acceptance of Meat Products" demonstrates that the value of different palatability traits has evolved over time. Moreover, consumer acceptance and preference are not solely determined by the inputs of the meat itself, but can also be influenced by various demographic factors. In addition, consumers' views of meat products vary regionally and by species. |
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