000 | 03638nam a22005895i 4500 | ||
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001 | 978-981-15-5573-2 | ||
003 | DE-He213 | ||
005 | 20240423125017.0 | ||
007 | cr nn 008mamaa | ||
008 | 200703s2020 si | s |||| 0|eng d | ||
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_a9789811555732 _9978-981-15-5573-2 |
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024 | 7 |
_a10.1007/978-981-15-5573-2 _2doi |
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_aLiu, Zhiyuan. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aRepresentation Learning for Natural Language Processing _h[electronic resource] / _cby Zhiyuan Liu, Yankai Lin, Maosong Sun. |
250 | _a1st ed. 2020. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2020. |
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300 |
_aXXIV, 334 p. 126 illus., 99 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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505 | 0 | _a1. 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. | |
506 | 0 | _aOpen Access | |
520 | _aThis 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. | ||
650 | 0 | _aNatural language processing (Computer science). | |
650 | 0 | _aComputational linguistics. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aData mining. | |
650 | 1 | 4 | _aNatural Language Processing (NLP). |
650 | 2 | 4 | _aComputational Linguistics. |
650 | 2 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
700 | 1 |
_aLin, Yankai. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
700 | 1 |
_aSun, Maosong. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789811555725 |
776 | 0 | 8 |
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_iPrinted edition: _z9789811555756 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-15-5573-2 |
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