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Register a new userMultilingual Negation Scope Resolution for Clinical Text
قرار النفي متعدد اللغات للنص السريري
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دقة النفي النفي هي مفتاح استخراج المعلومات عالية الجودة من النصوص السريرية، ولكن حتى الآن، كانت الجهود المبذولة لجعل المشفرين المستخدمة في نفي استخراج المعلومات، كانت تقتصر على اللغة الإنجليزية.نقدم نهجا عالميا لاستحقاق النرجب متعدد اللغات اللغوي، الذي يتغلب على عدم وجود بيانات تدريبية من خلال الاعتماد على الموارد المتفاوتة بلغات ومجالات مختلفة.نقيم نهجين للتعلم من هذه الموارد، والتدريب على البيانات والتدريب المجمع في إعداد التعلم متعدد المهام.تظهر تجاربنا أن دقة النطاق الصفرية في النص السريري ممكن، وأن الجمع بين الموارد المتاحة تعمل على تحسين الأداء في معظم الحالات.
Negation scope resolution is key to high-quality information extraction from clinical texts, but so far, efforts to make encoders used for information extraction negation-aware have been limited to English. We present a universal approach to multilingual negation scope resolution, that overcomes the lack of training data by relying on disparate resources in different languages and domains. We evaluate two approaches to learn from these resources, training on combined data and training in a multi-task learning setup. Our experiments show that zero-shot scope resolution in clinical text is possible, and that combining available resources improves performance in most cases.
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