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spancepredict: استخراج وثيقة تنبؤية يمتد بالاهتمام العصبي
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في العديد من تطبيقات معالجة اللغة الطبيعية، يمكن تحديد نص التنبؤ بنفس أهمية التنبؤات نفسها.عند التنبؤ بالتشخيصات الطبية، على سبيل المثال، تحديد المحتوى التنبئي في الملاحظات السريرية ليس فقط يعزز الترجمة الشفوية فحسب، بل يسمح أيضا بعوامل خطر غير معروفة وتصويرية (I.E.E.E.E) التي سيتم تحديدها.نحن هنا إضفاء الطابع الرسمي على هذه المشكلة كاستخراج تنبؤي ومعالجته باستخدام آلية بسيطة بناء على الانتباه الخطي.تحافظ طريقتنا على التباين، مما يسمح بالاستدلال القابل للتطوير عبر نزول التدرج الاستوكاستكي.علاوة على ذلك، يتحلل النموذج تنبؤات في مبلغ من مساهمات النص المميز.الأهم من ذلك، نحن نحتاج إلى ملصقات المستندات فقط، وليس الأمور في الحقيقة الأرضية.تشير النتائج إلى أن طرازنا يحدد يمتد يمتد متماسكة من الناحية الدلوية وتعيين درجاتهم التي تتفق مع التصنيفات البشرية، مع الحفاظ على أداء التصنيف.
In many natural language processing applications, identifying predictive text can be as important as the predictions themselves. When predicting medical diagnoses, for example, identifying predictive content in clinical notes not only enhances interpretability, but also allows unknown, descriptive (i.e., text-based) risk factors to be identified. We here formalize this problem as predictive extraction and address it using a simple mechanism based on linear attention. Our method preserves differentiability, allowing scalable inference via stochastic gradient descent. Further, the model decomposes predictions into a sum of contributions of distinct text spans. Importantly, we require only document labels, not ground-truth spans. Results show that our model identifies semantically-cohesive spans and assigns them scores that agree with human ratings, while preserving classification performance.
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