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Register a new userClassifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes
تصنيف العلاقات الجدلية باستخدام الآليات المنطقية ومخططات الجدال
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في حين حققت تعدين الوسيطة نجاحا كبيرا في تصنيف العلاقات الجدلية بين البيانات (الدعم والهجوم والمحايد)، لدينا فهم حسابي محدود للآليات المنطقية التي تشكل تلك العلاقات. تعتمد معظم الدراسات الحديثة على نماذج Black-Box، والتي ليست مرجعية على أنها مرغوبة. من ناحية أخرى، تستخدم الدراسات السابقة الميزات المعجمية البسيطة إلى حد ما، وفقدت العلاقات المنطقية بين البيانات. للتغلب على هذه القيود، يصنف عملنا علاقات جدلية بناء على أربع آليات منطقية ونظرية مستنيرة بين بيانين، وهي (ط) الاتساق الواقعي، (2) متماسك المعنويات، (3) العلاقة المعيارية (III)، (IV) العلاقة المعيارية. نوضح أن تفعيلنا لهذه الآليات المنطقية تصادف العلاقات الجدلية دون تدريب مباشر على البيانات المسمى بالعلاقات، أفضل بكثير من العديد من خطوط الأساس غير المدعومة. نوضح كذلك أن هذه الآليات أيضا تحسين المصنفين الخاضعين للإشراف من خلال تعليم التمثيل.
While argument mining has achieved significant success in classifying argumentative relations between statements (support, attack, and neutral), we have a limited computational understanding of logical mechanisms that constitute those relations. Most recent studies rely on black-box models, which are not as linguistically insightful as desired. On the other hand, earlier studies use rather simple lexical features, missing logical relations between statements. To overcome these limitations, our work classifies argumentative relations based on four logical and theory-informed mechanisms between two statements, namely, (i) factual consistency, (ii) sentiment coherence, (iii) causal relation, and (iv) normative relation. We demonstrate that our operationalization of these logical mechanisms classifies argumentative relations without directly training on data labeled with the relations, significantly better than several unsupervised baselines. We further demonstrate that these mechanisms also improve supervised classifiers through representation learning.
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