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Titlebook: Knowledge-augmented Methods for Natural Language Processing; Meng Jiang,Bill Yuchen Lin,Chenguang Zhu Book 2024 The Editor(s) (if applicab

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发表于 2025-3-21 18:10:31 | 显示全部楼层 |阅读模式
书目名称Knowledge-augmented Methods for Natural Language Processing
编辑Meng Jiang,Bill Yuchen Lin,Chenguang Zhu
视频videohttp://file.papertrans.cn/545/544248/544248.mp4
概述Reviews recent advances in natural language processing and focuses on the role of knowledge in language representation.Discusses the significance of knowledge in enabling higher levels of intelligence
丛书名称SpringerBriefs in Computer Science
图书封面Titlebook: Knowledge-augmented Methods for Natural Language Processing;  Meng Jiang,Bill Yuchen Lin,Chenguang Zhu Book 2024 The Editor(s) (if applicab
描述.Over 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 developmentsin 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
出版日期Book 2024
关键词Knowledge-augmented Methods; Commonsense Reasoning; Natural Language Understanding; Large Language Mode
版次1
doihttps://doi.org/10.1007/978-981-97-0747-8
isbn_softcover978-981-97-0749-2
isbn_ebook978-981-97-0747-8Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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SpringerBriefs in Computer Sciencehttp://image.papertrans.cn/k/image/544248.jpg
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978-981-97-0749-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Knowledge-augmented Methods for Natural Language Processing978-981-97-0747-8Series ISSN 2191-5768 Series E-ISSN 2191-5776
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2191-5768 cance of knowledge in enabling higher levels of intelligence.Over 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, Chat
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Introduction to Knowledge-augmented NLP,dge, e.g., knowledge graphs. The integration of these knowledge sources consists of three steps: (1) Grounding language into related knowledge; (2) Representing knowledge; and (3) Fusing knowledge representation into language models.
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