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Mastering text classificationcutting-edge NLP techniques /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mastering text classificationedited by Mourad Abbas.
Reminder of title:	cutting-edge NLP techniques /
other author:	Abbas, Mourad.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	vii, 142 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Text data mining.
Online resource:	https://doi.org/10.1007/978-3-031-93612-8
ISBN:	9783031936128$q(electronic bk.)

Mastering text classificationcutting-edge NLP techniques /
Mastering text classificationcutting-edge NLP techniques /[electronic resource] :edited by Mourad Abbas. - Cham :Springer Nature Switzerland :2025. - vii, 142 p. :ill. (some col.), digital ;24 cm. - Signals and communication technology,1860-4870. - Signals and communication technology..

Introduction -- Handling Realistic Label Noise in BERT Text Classification -- Discourse Relations Classification and Cross-Framework Discourse Relation Classification through the Lens of Cognitive Dimensions: An Empirical Investigation -- Representation Learning for Hierarchical Classification of Entity Titles -- DAP-LeR-DAug: Techniques for enhanced Online Sexism Detection -- Automatic Detection of Generalized Patterns of Vossian Antonomasia -- Exploring BERT Models for Part-of-Speech Tagging in the Algerian Dialect -- Deep Learning-Based Claim Matching with Multiple Negatives Training -- A Neural Network Approach to Ellipsis Detection in Ancient Greek -- Conclusion.

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.

ISBN: 9783031936128$q(electronic bk.)

Standard No.: 10.1007/978-3-031-93612-8doiSubjects--Topical Terms:

892173
Text data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Mastering text classificationcutting-edge NLP techniques /
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505 0 $a Introduction -- Handling Realistic Label Noise in BERT Text Classification -- Discourse Relations Classification and Cross-Framework Discourse Relation Classification through the Lens of Cognitive Dimensions: An Empirical Investigation -- Representation Learning for Hierarchical Classification of Entity Titles -- DAP-LeR-DAug: Techniques for enhanced Online Sexism Detection -- Automatic Detection of Generalized Patterns of Vossian Antonomasia -- Exploring BERT Models for Part-of-Speech Tagging in the Algerian Dialect -- Deep Learning-Based Claim Matching with Multiple Negatives Training -- A Neural Network Approach to Ellipsis Detection in Ancient Greek -- Conclusion.
520 $a 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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