Linguistic Resources for Natural Language Processing On the Necessity of Using Linguistic Methods to Develop NLP Software /
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 consequen...
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| Formato: | Recurso Electrónico livro electrónico |
| Idioma: | inglês |
| Publicado em: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Edição: | 1st ed. 2024. |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1007/978-3-031-43811-0 |
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Sumário:
- 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.