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Register a new userNADE: A Benchmark for Robust Adverse Drug Events Extraction in Face of Negations
nade: معيار لاستخراج أحداث المخدرات الضارة القوية في مواجهة النفي
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يمكن أن تفحص نماذج استخراج أحداث المخدرات السلبية (ADE) بسرعة مجموعات كبيرة من نصوص وسائل التواصل الاجتماعي، والكشف عن ذكرات التفاعلات السلبية ذات الصلة بالمخدرات وتحريك التحقيقات الطبية.ومع ذلك، على الرغم من التقدم الأخير في NLP، فإنه غير معروف حاليا إذا كانت هذه النماذج قوية في مواجهة النفي، والتي تنتشر عبر أصناف اللغة.في هذه الورقة، نقيم ثلاث أنظمة ثلاثية، تظهر هشاشةها ضد النفي، ثم نقدم استراتيجيتين ممكنين لزيادة متانة هذه النماذج: نهج خط أنابيب، بالاعتماد على مكون محدد للكشف عن النفي؛تكبير بيانات استخراج ADE لإنشاء عينات نفي بشكل مصطنع وتدريب النماذج الأخرى.نظهر أن كلا الاستراتيجيتين تجلب الزيادات الكبيرة في الأداء، مما أدى إلى خفض عدد الكيانات الزائفة المتوقعة من النماذج.سيتم إصدار بيانات DataSet و Code علنا لتشجيع البحث على الموضوع.
Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations. However, despite the recent advances in NLP, it is currently unknown if such models are robust in face of negation, which is pervasive across language varieties. In this paper we evaluate three state-of-the-art systems, showing their fragility against negation, and then we introduce two possible strategies to increase the robustness of these models: a pipeline approach, relying on a specific component for negation detection; an augmentation of an ADE extraction dataset to artificially create negated samples and further train the models. We show that both strategies bring significant increases in performance, lowering the number of spurious entities predicted by the models. Our dataset and code will be publicly released to encourage research on the topic.
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