title: Advancing Arabic Reverse Dictionary Systems: A Transformer-Based
    Approach with Dataset Construction Guidelines
summary: 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.
paper_authors._id: 68248d2c5d4294a3345b3889
paper_authors.name: Serry Sibaee
paper_authors.status: admin_assigned
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paper_authors.fullname: Serry Sibaee
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paper_authors.user: SerrySibaee
paper_authors._id: 68248d2c5d4294a3345b388a
paper_authors.name: Samar Ahmed
paper_authors._id: 68248d2c5d4294a3345b388b
paper_authors.name: Abdullah Al Harbi
paper_authors.status: admin_assigned
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paper_authors._id: 651452834a0c7cab164af487
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paper_authors.fullname: Abdullah Alharbi
paper_authors.type: user
paper_authors.user: harbiai
paper_authors._id: 68248d2c5d4294a3345b388c
paper_authors.name: Omer Nacar
paper_authors.status: claimed_verified
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paper_authors.fullname: Omartificial Intelligence Space
paper_authors.type: user
paper_authors.user: Omartificial-Intelligence-Space
paper_authors._id: 68248d2c5d4294a3345b388d
paper_authors.name: Adel Ammar
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T12:55:04.658Z
paper_authors._id: 647c60cd36e109abce3b3b15
paper_authors.avatarUrl: /avatars/d702ec97cc2cde5ca7b5d16feb5f45c9.svg
paper_authors.fullname: Adel Ammar
paper_authors.type: user
paper_authors.user: ammaradel
paper_authors._id: 68248d2c5d4294a3345b388e
paper_authors.name: Yasser Habashi
paper_authors._id: 68248d2c5d4294a3345b388f
paper_authors.name: Wadii Boulila
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T12:55:18.105Z
paper_authors._id: 67bde4936c7c7de3a68608d1
paper_authors.avatarUrl: /avatars/e08afba50d8aae8f7698ccba4c1e7b4a.svg
paper_authors.fullname: Wadii Boulila
paper_authors.type: user
paper_authors.user: wboulila
paper_title: Advancing Arabic Reverse Dictionary Systems: A Transformer-Based
    Approach with Dataset Construction Guidelines
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: transformer-based semi-encoder neural network geometrically decreasing layers ArabicRD ARBERTv2 multilingual embeddings RDTL training pipelines lexicographic definitions dataset quality