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| 001 | 978-981-97-0747-8 | ||
| 003 | DE-He213 | ||
| 005 | 20240730171807.0 | ||
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| 008 | 240408s2024 si | s |||| 0|eng d | ||
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_a10.1007/978-981-97-0747-8 _2doi |
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_a006.35 _223 |
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_aJiang, Meng. _eauthor. _0(orcid) _10000-0002-3009-519X _4aut _4http://id.loc.gov/vocabulary/relators/aut _9100406 |
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| 245 | 1 | 0 |
_aKnowledge-augmented Methods for Natural Language Processing _h[electronic resource] / _cby Meng Jiang, Bill Yuchen Lin, Shuohang Wang, Yichong Xu, Wenhao Yu, Chenguang Zhu. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2024. |
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| 300 |
_aIX, 95 p. 18 illus., 15 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aSpringerBriefs in Computer Science, _x2191-5776 |
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| 505 | 0 | _aChapter 1. Introduction to Knowledge-augmented NLP -- Chapter 2. Knowledge Sources -- Chapter 3. Knowledge-augmented Methods for Natural Language Understanding -- Chapter 4. Knowledge-augmented Methods for Natural Language Generation -- Chapter 5. Augmenting NLP Models with Commonsense Knowledge -- Chapter 6. Summary and Future Directions. | |
| 520 | _aOver the last few years, natural language processing has seen remarkable progress due to the emergence of larger-scale models, better training techniques, and greater availability of data. Examples of these advancements include GPT-4, ChatGPT, and other pre-trained language models. These models are capable of characterizing linguistic patterns and generating context-aware representations, resulting in high-quality output. However, these models rely solely on input-output pairs during training and, therefore, struggle to incorporate external world knowledge, such as named entities, their relations, common sense, and domain-specific content. Incorporating knowledge into the training and inference of language models is critical to their ability to represent language accurately. Additionally, knowledge is essential in achieving higher levels of intelligence that cannot be attained through statistical learning of input text patterns alone. In this book, we will review recent developments in the field of natural language processing, specifically focusing on the role of knowledge in language representation. We will examine how pre-trained language models like GPT-4 and ChatGPT are limited in their ability to capture external world knowledge and explore various approaches to incorporate knowledge into language models. Additionally, we will discuss the significance of knowledge in enabling higher levels of intelligence that go beyond statistical learning on input text patterns. Overall, this survey aims to provide insights into the importance of knowledge in natural language processing and highlight recent advances in this field. | ||
| 650 | 0 |
_aNatural language processing (Computer science). _94741 |
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| 650 | 0 |
_aComputational linguistics. _96146 |
|
| 650 | 0 |
_aData mining. _93907 |
|
| 650 | 1 | 4 |
_aNatural Language Processing (NLP). _931587 |
| 650 | 2 | 4 |
_aComputational Linguistics. _96146 |
| 650 | 2 | 4 |
_aData Mining and Knowledge Discovery. _9100408 |
| 700 | 1 |
_aLin, Bill Yuchen. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9100410 |
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| 700 | 1 |
_aWang, Shuohang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9100411 |
|
| 700 | 1 |
_aXu, Yichong. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9100414 |
|
| 700 | 1 |
_aYu, Wenhao. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9100415 |
|
| 700 | 1 |
_aZhu, Chenguang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9100416 |
|
| 710 | 2 |
_aSpringerLink (Online service) _9100417 |
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| 773 | 0 | _tSpringer Nature eBook | |
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_iPrinted edition: _z9789819707461 |
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_aSpringerBriefs in Computer Science, _x2191-5776 _9100419 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-97-0747-8 |
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