Advancing Arabic Reverse Dictionary Systems: A Transformer-Based
  Approach with Dataset Construction Guidelines
This study addresses the critical gap in Arabic natural language processing
by developing an effective Arabic Reverse Dictionary (RD) system that enables
users to find words based on their descriptions or meanings. We present a novel
transformer-based approach with a semi-encoder neural network architecture
featuring geometrically decreasing layers that achieves state-of-the-art
results for Arabic RD tasks. Our methodology incorporates a comprehensive
dataset construction process and establishes formal quality standards for
Arabic lexicographic definitions. Experiments with various pre-trained models
demonstrate that Arabic-specific models significantly outperform general
multilingual embeddings, with ARBERTv2 achieving the best ranking score
(0.0644). Additionally, we provide a formal abstraction of the reverse
dictionary task that enhances theoretical understanding and develop a modular,
extensible Python library (RDTL) with configurable training pipelines. Our
analysis of dataset quality reveals important insights for improving Arabic
definition construction, leading to eight specific standards for building
high-quality reverse dictionary resources. This work contributes significantly
to Arabic computational linguistics and provides valuable tools for language
learning, academic writing, and professional communication in Arabic.
68248d2c5d4294a3345b3889
Serry Sibaee
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2025-05-14T12:54:34.701Z
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Serry Sibaee
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SerrySibaee
68248d2c5d4294a3345b388a
Samar Ahmed
68248d2c5d4294a3345b388b
Abdullah Al Harbi
admin_assigned
2025-05-14T12:54:54.777Z
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Abdullah Alharbi
user
harbiai
68248d2c5d4294a3345b388c
Omer Nacar
claimed_verified
2025-05-15T10:32:33.539Z
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Omartificial Intelligence Space
user
Omartificial-Intelligence-Space
68248d2c5d4294a3345b388d
Adel Ammar
admin_assigned
2025-05-14T12:55:04.658Z
647c60cd36e109abce3b3b15
/avatars/d702ec97cc2cde5ca7b5d16feb5f45c9.svg
Adel Ammar
user
ammaradel
68248d2c5d4294a3345b388e
Yasser Habashi
68248d2c5d4294a3345b388f
Wadii Boulila
admin_assigned
2025-05-14T12:55:18.105Z
67bde4936c7c7de3a68608d1
/avatars/e08afba50d8aae8f7698ccba4c1e7b4a.svg
Wadii Boulila
user
wboulila
Advancing Arabic Reverse Dictionary Systems: A Transformer-Based
  Approach with Dataset Construction Guidelines
This study addresses the critical gap in Arabic natural language processing
by developing an effective Arabic Reverse Dictionary (RD) system that enables
users to find words based on their descriptions or meanings. We present a novel
transformer-based approach with a semi-encoder neural network architecture
featuring geometrically decreasing layers that achieves state-of-the-art
results for Arabic RD tasks. Our methodology incorporates a comprehensive
dataset construction process and establishes formal quality standards for
Arabic lexicographic definitions. Experiments with various pre-trained models
demonstrate that Arabic-specific models significantly outperform general
multilingual embeddings, with ARBERTv2 achieving the best ranking score
(0.0644). Additionally, we provide a formal abstraction of the reverse
dictionary task that enhances theoretical understanding and develop a modular,
extensible Python library (RDTL) with configurable training pipelines. Our
analysis of dataset quality reveals important insights for improving Arabic
definition construction, leading to eight specific standards for building
high-quality reverse dictionary resources. This work contributes significantly
to Arabic computational linguistics and provides valuable tools for language
learning, academic writing, and professional communication in Arabic.
A transformer-based reverse dictionary system with a semi-encoder architecture achieves superior results for Arabic RD tasks, outperforming multilingual embeddings, and provides quality standards and a Python library for development.
transformer-based semi-encoder neural network geometrically decreasing layers ArabicRD ARBERTv2 multilingual embeddings RDTL training pipelines lexicographic definitions dataset quality