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Register a new userCodRED: A Cross-Document Relation Extraction Dataset for Acquiring Knowledge in the Wild
codred: مجموعة بيانات استخراج العلاقة بين المستندات للحصول على المعرفة في البرية
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تركز أساليب استخراج العلاقة الحالية (إعادة) عادة على استخراج الحقائق العلائقية بين أزواج الكيان داخل جمل أو مستندات واحدة.ومع ذلك، لا يمكن استنتاج كمية كبيرة من الحقائق العلائقية في قواعد المعرفة إلا في جميع الوثائق في الممارسة.في هذا العمل، نقدم مشكلة إعادة الوثيقة العملية، مما يجعل خطوة أولية نحو اكتساب المعرفة في البرية.لتسهيل البحث، نقوم ببناء أول مجموعة بيانات عبر الوثيقة المشروح البشرية.مقارنة مع مجموعات البيانات الحالية، تقدم Codred تحديين رئيسيين: بالنظر إلى كيانين، (1) يتطلب إيجاد المستندات ذات الصلة التي يمكن أن توفر أدلة لتحديد علاقاتها؛(2) يتطلب التفكير في مستندات متعددة لاستخراج الحقائق العلائقية.نقوم بإجراء تجارب شاملة لإظهار أن Codred هو تحدي أساليب إعادة الخدمات القائمة بما في ذلك النماذج القاعدة القائمة على بيرت.
Existing relation extraction (RE) methods typically focus on extracting relational facts between entity pairs within single sentences or documents. However, a large quantity of relational facts in knowledge bases can only be inferred across documents in practice. In this work, we present the problem of cross-document RE, making an initial step towards knowledge acquisition in the wild. To facilitate the research, we construct the first human-annotated cross-document RE dataset CodRED. Compared to existing RE datasets, CodRED presents two key challenges: Given two entities, (1) it requires finding the relevant documents that can provide clues for identifying their relations; (2) it requires reasoning over multiple documents to extract the relational facts. We conduct comprehensive experiments to show that CodRED is challenging to existing RE methods including strong BERT-based models.
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