Overview
- Addresses the topic of multiword units in NLP software and the issue low-resource languages
- Discusses training corpus-based approaches and explains the intrinsic value of linguistic formalization
- Shows how carefully handcrafted linguistic resources can successfully enhance NLP software applications
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About this book
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.
Keywords
- Named Entity Recognition
- Natural Language Processing
- Text Generation
- Training Corpora
- Statistical Methods
- Stochastic Methods
- Multiword Units
- Corpus Linguistics
- Automatic Disambiguation
- Elvex Formalism
- Machine Translation
- Text-to-Speech Synthesizer
- NooJ Dictionaries
- Quechua Morphology
- Low-resource Languages
- Information Retrieval
- Lexicon-Grammar
- Syntactic Parsing
Table of contents (11 chapters)
-
Introduction
-
Developing Linguistic-Based NLP Software
-
Linguistic Resources for Low-Resource Languages
-
Processing Multiword Units: The Linguistic Approach
Editors and Affiliations
About the editor
Max Silberztein is a Professor of Linguistics, Computational Linguistics and Computer Science at the Université de Franche-Comté. He is the author of the three NLP software platforms (INTEX, NooJ and ATISHS), two books (Dictionnaires électroniques et analyse automatique de textes: le système INTEX, Masson 1993; Formalizing Natural Languages: the NooJ approach, Wiley 2016), and editor of over 15 volumes of selected Proceedings in Springer CCIS and LNCS series.
Bibliographic Information
Book Title: Linguistic Resources for Natural Language Processing
Book Subtitle: On the Necessity of Using Linguistic Methods to Develop NLP Software
Editors: Max Silberztein
DOI: https://doi.org/10.1007/978-3-031-43811-0
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
Hardcover ISBN: 978-3-031-43810-3Published: 14 March 2024
Softcover ISBN: 978-3-031-43813-4Due: 28 March 2025
eBook ISBN: 978-3-031-43811-0Published: 13 March 2024
Edition Number: 1
Number of Pages: XXII, 217
Number of Illustrations: 17 b/w illustrations, 101 illustrations in colour
Topics: Natural Language Processing (NLP), Computational Linguistics, Artificial Intelligence, Digital Humanities