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Register a new userLow-resource Taxonomy Enrichment with Pretrained Language Models
تخصيص تصنيف التصنيف المنخفض مع نماذج اللغة المحددة مسبقا
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التصنيفات هي تمثيل رمزي للعلاقات الهرمية بين المصطلحات أو الكيانات. في حين أن التصنيفات مفيدة في تطبيقات واسعة، فإن تحديثها أو الحفاظ عليها يدويا كثيفة العمالة وصعبة الحجم في الممارسة العملية. تفشل الأساليب الإشرافية التقليدية لهذه المهمة التخصيب هذه في العثور على والدي الأمثل للمصطلحات الجديدة في إعدادات الموارد المنخفضة حيث تتوفر تصنيفات صغيرة فقط بسبب التجاوز عن العلاقات الهرمية في التصنيفات. لمعالجة مشكلة تخصيب التصنيف المنخفض للموارد، نقترح Musubu، وهو إطار فعال لإثراء التصنيف في إعدادات الموارد المنخفضة مع نماذج اللغة المحددة مسبقا (LMS) كقواعد المعرفة للتعويض عن نقص المعلومات. يستفيد Musubu مصنف قائم على LM لتحديد ما إذا كان أزواج المصطلح المدبأ أو عدم وجود علاقات هرمية. يستخدم Musubu أيضا أنماطا هارا لتوليد استفسارات للاستفادة من المعرفة الضمنية من LM بكفاءة من أجل التنبؤ الأكثر دقة. إننا نوضح تجريبيا فعالية طريقتنا في تجارب واسعة النطاق بشأن التصنيفات من كل من مهمة Semeval ومجموعات بيانات التجزئة العالمية الحقيقية.
Taxonomies are symbolic representations of hierarchical relationships between terms or entities. While taxonomies are useful in broad applications, manually updating or maintaining them is labor-intensive and difficult to scale in practice. Conventional supervised methods for this enrichment task fail to find optimal parents of new terms in low-resource settings where only small taxonomies are available because of overfitting to hierarchical relationships in the taxonomies. To tackle the problem of low-resource taxonomy enrichment, we propose Musubu, an efficient framework for taxonomy enrichment in low-resource settings with pretrained language models (LMs) as knowledge bases to compensate for the shortage of information. Musubu leverages an LM-based classifier to determine whether or not inputted term pairs have hierarchical relationships. Musubu also utilizes Hearst patterns to generate queries to leverage implicit knowledge from the LM efficiently for more accurate prediction. We empirically demonstrate the effectiveness of our method in extensive experiments on taxonomies from both a SemEval task and real-world retailer datasets.
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https://aclanthology.org/
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