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024 7 _a10.1007/978-981-97-0747-8
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082 0 4 _a006.35
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100 1 _aJiang, Meng.
_eauthor.
_0(orcid)
_10000-0002-3009-519X
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9100406
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.
300 _aIX, 95 p. 18 illus., 15 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _aSpringerBriefs in Computer Science,
_x2191-5776
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
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
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
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9789819707485
776 0 8 _iPrinted edition:
_z9789819707492
830 0 _aSpringerBriefs in Computer Science,
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_9100419
856 4 0 _uhttps://doi.org/10.1007/978-981-97-0747-8
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