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Register a new userLeveraging Capsule Routing to Associate Knowledge with Medical Literature Hierarchically
الاستفادة من كبسولة توجيه المعرفة العارضة مع الأدب الطبي هرمي
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إن دمج المعرفة في نص هو وسيلة واعدة لإثراء التمثيل النصي، خاصة في المجال الطبي. ومع ذلك، فإن المعرفة غير المتمايزة لا تخلط بين تمثيل النص فحسب، بل تستورد أيضا ضوضاء غير متوقعة. في هذه الورقة، لتخفيف هذه المشكلة، نقترح الاستفادة من كبسولة التوجيه لربط المعرفة مع الأدب الطبي هرمي (يسمى Hicapsrkl). أولا، تستخرج Hicapsrkl شظايا نصية مصممة تجريبية من الأدب الطبي وتكررها في تمثيلات شظية على التوالي. ثانيا، يتم تطبيق خوارزمية التوجيه الكبسولة على تمثيلين شظيين. من خلال الحوسبة الكبسولة والتوجيه الديناميكي، تتم معالجة كل تمثيل في تمثيل جديد (يشار إليه كتمثيل قبعات)، ونحن ندمج تمثيلات قبعات ككبار معلومات للمعرفة العاملية بالأدب الطبي هرمي. أخيرا، يتم التحقق من صحة Hicapsrkl عند التنبؤ بالأهمية ومجموعات اختبار استرجاع الأدب الطبي. تظهر النتائج والتحليلات التجريبية أن Hicapsrklcan أكثر دقة المعرفة مع الأدب الطبي من الأساليب الرئيسية. باختصار، يمكن أن يساعد Hicapsrkl بكفاءة في اختيار المعرفة الأكثر صلة بالأدب الطبي، والتي قد تكون محاولة بديلة لتحسين تمثيل النص المستند إلى المعرفة. يتم إصدار شفرة المصدر على Github.
Integrating knowledge into text is a promising way to enrich text representation, especially in the medical field. However, undifferentiated knowledge not only confuses the text representation but also imports unexpected noises. In this paper, to alleviate this problem, we propose leveraging capsule routing to associate knowledge with medical literature hierarchically (called HiCapsRKL). Firstly, HiCapsRKL extracts two empirically designed text fragments from medical literature and encodes them into fragment representations respectively. Secondly, the capsule routing algorithm is applied to two fragment representations. Through the capsule computing and dynamic routing, each representation is processed into a new representation (denoted as caps-representation), and we integrate the caps-representations as information gain to associate knowledge with medical literature hierarchically. Finally, HiCapsRKL are validated on relevance prediction and medical literature retrieval test sets. The experimental results and analyses show that HiCapsRKLcan more accurately associate knowledge with medical literature than mainstream methods. In summary, HiCapsRKL can efficiently help selecting the most relevant knowledge to the medical literature, which may be an alternative attempt to improve knowledge-based text representation. Source code is released on GitHub.
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