モロッコの方言における人工知能による自然言語処理によるエンティティ認識の発表
Wala Bin Subait1, Nazir Ahmad2, Muhammad Swaileh A Alzaidi3
1Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Scientific reports
|August 22, 2025
まとめ
この研究は,モロッコの方言でアラビア語のエンティティ認識 (NER) のための新しいAI技術を導入し,正確さを大幅に改善します. アラビア語のテキストからエンティティを抽出する精度が向上します.
科学分野:
- 自然言語処理 (NLP)
- 人工知能 (AI)
- ディープラーニング (DL)
背景:
- 命名されたエンティティ認識 (NER) は,データ取得などのNLPアプリケーションにとって不可欠です.
- アラビア語NERは,特に方言の言語的複雑さにより,ユニークな課題を提示しています.
- 既存のディープラーニングモデルは,方言を無視してモダンスタンダードアラビア語 (MSA) に焦点を当てている.
研究 の 目的:
- モロッコの方言で,アラビア語のエンティティ認識 (NGOAI-ANER) のための人工知能によるノース・ゴーショーク最適化という新しい技術を導入する.
- アラビア語 NER システムの精度と効率を高める.
- 先進的なAIを用いて方言的なアラビア語NERの複雑さを解決する.
主な方法:
- 文字を意味学的に表現するための文字埋め込み技術.
- スタック・アテンション・ロング・ショート・ターム・メモリー (SALSTM) は,正確なエンティティの識別を可能にします.
- DLモデルのハイパーパラメータチューニングのためのNorthern Goshawk Optimization (NGO) モデル.
主要な成果:
- NGOAI-ANER技術は,DarNERcorpのデータセットで優れたパフォーマンスを示しました.
- 97.86%の精度を達成し,既存のアラビア語NERアプローチを上回りました.
- 提案されたAI駆動のNERモデルの有効性とスケーラビリティを検証した.
結論:
- NGOAI-ANER技術は,方言的なアラビア語のNERに顕著な進歩をもたらします.
- ディープラーニングと最適化アルゴリズムの統合は,NERの課題を効果的に解決します.
- このアプローチは,アラビア語名称のエンティティ認識のためのスケーラブルで正確なソリューションを提供します.
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