05044nam a22005415i 4500 978-3-031-43811-0 DE-He213 20251030111718.0 cr nn 008mamaa 240313s2024 sz | s |||| 0|eng d 9783031438110 978-3-031-43811-0 10.1007/978-3-031-43811-0 doi QA76.9.N38 UYQL bicssc COM073000 bisacsh UYQL thema Linguistic Resources for Natural Language Processing [electronic resource] : On the Necessity of Using Linguistic Methods to Develop NLP Software / edited by Max Silberztein. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XXII, 217 p. 118 illus., 101 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda In honor of Peter -- Foreword. - Preface -- About this book. Part 1. Introduction -- 1. The Limitations of Corpus-based Methods in NLP -- Part 2 -- 2. Developing Linguistic-based NLP Software -- 3. Linguistic Resources for the Automatic Generation of Texts in Natural Language -- 4. Towards a More Efficient Arabic-French Translation -- 5. Linguistic Resources and Methods and Algorithms for Belarusian Natural Language Processing -- Part 3 -- Linguistic Resources for Low-resource Languages -- 6. A New Set of Linguistic Resources for Ukrainian -- 7. Formalization of the Quechua Morphology -- 8. The Challenging Task of Translating the Language of Tango -- 9. A Polylectal Linguistic Resource for Rromani -- Part 4. Processing Multiword Units: The Linguistic Approach -- 10. Using Linguistic Criteria to Define Multiword Units -- 11. A Linguistic Approach to English Phrasal Verbs -- 12. Analysis of Indonesian Multiword Expressions: Linguistic vs Data-driven Approach. Empirical — data-driven, neural network-based, probabilistic, and statistical — methods seem to be the modern trend. Recently, OpenAI’s ChatGPT, Google’s Bard and Microsoft’s Sydney chatbots have been garnering a lot of attention for their detailed answers across many knowledge domains. In consequence, most AI researchers are no longer interested in trying to understand what common intelligence is or how intelligent agents construct scenarios to solve various problems. Instead, they now develop systems that extract solutions from massive databases used as cheat sheets. In the same manner, Natural Language Processing (NLP) software that uses training corpora associated with empirical methods are trendy, as most researchers in NLP today use large training corpora, always to the detriment of the development of formalized dictionaries and grammars. Not questioning the intrinsic value of many software applications based on empirical methods, this volume aims at rehabilitating the linguistic approach to NLP. In an introduction, the editor uncovers several limitations and flaws of using training corpora to develop NLP applications, even the simplest ones, such as automatic taggers. The first part of the volume is dedicated to showing how carefully handcrafted linguistic resources could be successfully used to enhance current NLP software applications. The second part presents two representative cases where data-driven approaches cannot be implemented simply because there is not enough data available for low-resource languages. The third part addresses the problem of how to treat multiword units in NLP software, which is arguably the weakest point of NLP applications today but has a simple and elegant linguistic solution. It is the editor's belief that readers interested in Natural Language Processing will appreciate the importance of this volume, both for its questioning of the training corpus-based approaches and for the intrinsic value of the linguistic formalization and the underlying methodology presented. Natural language processing (Computer science). 65131 Computational linguistics. Artificial intelligence. 58441 Digital humanities. Natural Language Processing (NLP). 66164 Computational Linguistics. Artificial Intelligence. 58441 Digital Humanities. Silberztein, Max. editor. edt http://id.loc.gov/vocabulary/relators/edt 72904 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031438103 Printed edition: 9783031438127 Printed edition: 9783031438134 https://doi.org/10.1007/978-3-031-43811-0 ZDB-2-SCS ZDB-2-SXCS 58279 58279