Mastering Text Classification Cutting-Edge NLP Techniques /
This book provides cutting-edge natural language processing (NLP) techniques to unlock the power of text data. It presents advanced methods for various text classification tasks, like discourse relation classification, classification in large taxonomies, and leveraging disagreement between annotator...
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| Формат: | Электронный ресурс eКнига |
| Язык: | английский |
| Опубликовано: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Редактирование: | 1st ed. 2025. |
| Серии: | Signals and Communication Technology,
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| Предметы: | |
| Online-ссылка: | https://doi.org/10.1007/978-3-031-93612-8 |
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| Итог: | This book provides cutting-edge natural language processing (NLP) techniques to unlock the power of text data. It presents advanced methods for various text classification tasks, like discourse relation classification, classification in large taxonomies, and leveraging disagreement between annotators for text classification. This book equips readers whether they are researchers or professionals, looking to apply NLP in real-world settings, with the latest advancements, and gives them the opportunity to explore techniques to handle limited data, and harness the power of pre-trained language models like BERT. By the end, readers will be equipped to tackle specific text classification challenges and advance the field of NLP. Explores methods for a variety of natural language processing (NLP) tasks for mastering text classification; Showcases applications of NLP in areas like named entity recognition for Arabic and its dialetcs and tackling multilingual and multitask text classification; Presents challenges like limited annotated data and processing less-resourced languages. |
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| Объем: | 1 online resource (VII, 142 p. 51 illus.). 36 illus. in color. |
| ISBN: | 9783031936128 |
| ISSN: | 1860-4870 |
| DOI: | 10.1007/978-3-031-93612-8 |