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Register a new userNoisy-Labeled NER with Confidence Estimation
صاخبة المسمى ner مع تقدير الثقة
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أظهرت الدراسات الحديثة في التعلم العميق تقدما كبيرا في التعرف على الكيان المسمى (NER).ومع ذلك، تفترض أن معظم الأعمال الموجودة تفرض شرحا نظيفا للبيانات، في حين أن سيناريوهات العالم الواقعي تشتمل عادة على كمية كبيرة من الضوضاء من مجموعة متنوعة من المصادر (E.G.، الزائفة أو الضعيفة أو الشريحية البعيدة).يدرس هذا العمل ner تحت إعداد صاخبة تحمل تصنيف مع تقدير الثقة المعايرة.بناء على الملاحظات التجريبية لمختلف الديناميات التدريبية للتسميات الصاخبة والنظيفة، نقترح استراتيجيات لتقدير درجات الثقة بناء على افتراضات الاستقلال المحلية والعالمية.نحن نتهم جزئيا تسميات الثقة المنخفضة بنموذج CRF.نقترح طريقة معايرة لعشرات الثقة بناء على هيكل ملصقات الكيان.نحن ندمج نهجنا في إطار التدريب الذاتي لتعزيز الأداء.تجارب في إعدادات صاخبة عامة مع أربع لغات وإعدادات المسمى المسمى أظهرت فعالية طريقتنا.
Recent studies in deep learning have shown significant progress in named entity recognition (NER). However, most existing works assume clean data annotation, while real-world scenarios typically involve a large amount of noises from a variety of sources (e.g., pseudo, weak, or distant annotations). This work studies NER under a noisy labeled setting with calibrated confidence estimation. Based on empirical observations of different training dynamics of noisy and clean labels, we propose strategies for estimating confidence scores based on local and global independence assumptions. We partially marginalize out labels of low confidence with a CRF model. We further propose a calibration method for confidence scores based on the structure of entity labels. We integrate our approach into a self-training framework for boosting performance. Experiments in general noisy settings with four languages and distantly labeled settings demonstrate the effectiveness of our method.
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