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الفرم الديناميكي في التعرف على الكيان المسمى للنصوص العربية التاريخية
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يتزايد استخدام التعرف على الكيان المسمى (NER) على النصوص العربية القديمة بشكل مطرد.ومع ذلك، فقد تم تطوير معظم الأدوات لإرجاع اللغة الإنجليزية الحديثة أو تدربت على وثائق اللغة الإنجليزية وهي محدودة للنص العربي التاريخي.حتى أدوات NER العربية غالبا ما تدرب على نص حديث من مصادر الويب، مما يجعل مناسبا له بمهمة تاريخية مشكوك فيها.لتخفيف ندرة الموارد العربية السعودية العربية، نقترح نموذج فرقة ديناميكية باستخدام العديد من المتعلمين.يتم تحقيق الجانب الديناميكي من خلال الاستفادة من التنبؤ والميزات على نتائج خوارزمية NER التي حددت التي أجريت بشكل أفضل على مهمة محددة في الوقت الفعلي.نقوم بتقييم نهجنا ضد أحدث أساليب النيران العربية والثابتة من أساليب الفرقة الثابتة عبر مهمة تاريخية تاريخية جديدة التي أنشأناها.تظهر نتائجنا أن نهجنا يحسن على أحدث ويودر من 0.8 درجة مئوية بشأن هذه المهمة الصعبة.
The use of Named Entity Recognition (NER) over archaic Arabic texts is steadily increasing. However, most tools have been either developed for modern English or trained over English language documents and are limited over historical Arabic text. Even Arabic NER tools are often trained on modern web-sourced text, making their fit for a historical task questionable. To mitigate historic Arabic NER resource scarcity, we propose a dynamic ensemble model utilizing several learners. The dynamic aspect is achieved by utilizing predictors and features over NER algorithm results that identify which have performed better on a specific task in real-time. We evaluate our approach against state-of-the-art Arabic NER and static ensemble methods over a novel historical Arabic NER task we have created. Our results show that our approach improves upon the state-of-the-art and reaches a 0.8 F-score on this challenging task.
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