LEADER 04215nam 22006735 450 001 9910847585803321 005 20250807143417.0 010 $a981-9707-47-1 024 7 $a10.1007/978-981-97-0747-8 035 $a(MiAaPQ)EBC31260734 035 $a(Au-PeEL)EBL31260734 035 $a(CKB)31428317300041 035 $a(DE-He213)978-981-97-0747-8 035 $a(MiAaPQ)EBC31319766 035 $a(Au-PeEL)EBL31319766 035 $a(EXLCZ)9931428317300041 100 $a20240408d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aKnowledge-augmented Methods for Natural Language Processing /$fby Meng Jiang, Bill Yuchen Lin, Shuohang Wang, Yichong Xu, Wenhao Yu, Chenguang Zhu 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (101 pages) 225 1 $aSpringerBriefs in Computer Science,$x2191-5776 311 08$a981-9707-46-3 311 08$a981-9707-49-8 327 $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. 330 $aOver the last few years, natural language processing has seen remarkable progress due to the emergence of larger-scale models, better training techniques, and greater availability of data. Examples of these advancements include GPT-4, ChatGPT, and other pre-trained language models. These models are capable of characterizing linguistic patterns and generating context-aware representations, resulting in high-quality output. However, these models rely solely on input-output pairs during training and, therefore, struggle to incorporate external world knowledge, such as named entities, their relations, common sense, and domain-specific content. Incorporating knowledge into the training and inference of language models is critical to their ability to represent language accurately. Additionally, knowledge is essential in achieving higher levels of intelligence that cannot be attained through statistical learning of input text patterns alone. In this book, we will review recent developments in the field of natural language processing, specifically focusing on the role of knowledge in language representation. We will examine how pre-trained language models like GPT-4 and ChatGPT are limited in their ability to capture external world knowledge and explore various approaches to incorporate knowledge into language models. Additionally, we will discuss the significance of knowledge in enabling higher levels of intelligence that go beyond statistical learning on input text patterns. Overall, this survey aims to provide insights into the importance of knowledge in natural language processing and highlight recent advances in this field. 410 0$aSpringerBriefs in Computer Science,$x2191-5776 606 $aNatural language processing (Computer science) 606 $aComputational linguistics 606 $aData mining 606 $aNatural Language Processing (NLP) 606 $aComputational Linguistics 606 $aData Mining and Knowledge Discovery 615 0$aNatural language processing (Computer science) 615 0$aComputational linguistics. 615 0$aData mining. 615 14$aNatural Language Processing (NLP). 615 24$aComputational Linguistics. 615 24$aData Mining and Knowledge Discovery. 676 $a006.35 676 $a000 700 $aJiang$b Meng$01736343 701 $aLin$b Bill Yuchen$01736344 701 $aWang$b Shuohang$01736345 701 $aXu$b Yichong$01736346 701 $aYu$b Wenhao$01736347 701 $aZhu$b Chenguang$01736348 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910847585803321 996 $aKnowledge-augmented Methods for Natural Language Processing$94156199 997 $aUNINA