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Linguistic Resources for Natural Language Processing On the Necessity of Using Linguistic Methods to Develop NLP Software

Title
Linguistic Resources for Natural Language Processing [electronic resource] : On the Necessity of Using Linguistic Methods to Develop NLP Software / edited by Max Silberztein.
ISBN
9783031438110
Edition
1st ed. 2024.
Publication
Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Physical Description
1 online resource (XXII, 217 p.) 118 illus., 101 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
April 10, 2024
Contents
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.
Also listed under
Silberztein, Max. editor.
SpringerLink (Online service)
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