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Register a new userSeed Word Selection for Weakly-Supervised Text Classification with Unsupervised Error Estimation
اختيار كلمة البذور لتصنيف النص الإشراف ضعيف مع تقدير الأخطاء غير المركبات
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يهدف تصنيف النص الخاضع للإشراف ضعيف إلى حثص نصوص النص من عدد قليل من كلمات البذور التي توفرها المستخدم. غالبية العمل العظمى من العمل السابق يفترض أن كلمات البذور عالية الجودة تعطى. ومع ذلك، فإن كلمات البذور المشروح للخبراء في بعض الأحيان غير تافهة للتوصل إلى ذلك. علاوة على ذلك، في إعداد التعلم الإشراف ضعيف، ليس لدينا أي وثيقة مسمى لقياس فعالية كلمات البذور، مما يجعل عملية اختيار كلمة البذور المشي في الظلام ". في هذا العمل، نقوم بإزالة الحاجة إلى كلمات البذور التي يديرها الخبراء من قبل كلمات البذور المرشحة الأولى (صاخبة) المرتبطة بأسماء الفئات. ثم تدريب نماذج مؤقتة بكلمات البذور المرشحة الفردية. أخيرا، نقدر معدل خطأ النماذج المؤقتة بطريقة غير منشأة. تتم إضافة كلمات البذور التي تسفر عن أدنى معدلات الخطأ المقدرة إلى مجموعة كلمات البذور النهائية. يوضح تقييم شامل لستة مهام التصنيف الثنائية على أربع مجموعات بيانات شائعة أن الطريقة المقترحة تتفوق على خط أساس باستخدام كلمات البذور اسم الفئة فقط والحصول على أداء مماثل كظرف باستخدام كلمات البذور المشروح من الخبراء.
Weakly-supervised text classification aims to induce text classifiers from only a few user-provided seed words. The vast majority of previous work assumes high-quality seed words are given. However, the expert-annotated seed words are sometimes non-trivial to come up with. Furthermore, in the weakly-supervised learning setting, we do not have any labeled document to measure the seed words' efficacy, making the seed word selection process a walk in the dark''. In this work, we remove the need for expert-curated seed words by first mining (noisy) candidate seed words associated with the category names. We then train interim models with individual candidate seed words. Lastly, we estimate the interim models' error rate in an unsupervised manner. The seed words that yield the lowest estimated error rates are added to the final seed word set. A comprehensive evaluation of six binary classification tasks on four popular datasets demonstrates that the proposed method outperforms a baseline using only category name seed words and obtained comparable performance as a counterpart using expert-annotated seed words.
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