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Register a new userA Graph-Based Neural Model for End-to-End Frame Semantic Parsing
نموذج عصبي مقره الرسم البياني لتحليل الإطار الدلالي نهاية إلى نهاية
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يعد تحليل الإطار الدلالي مهمة تحليل دلالية تعتمد على Framenet التي تلقت اهتماما كبيرا مؤخرا.تتضمن المهمة عادة ثلاث مجموعات فرعية بالتتابع: (1) التعرف المستهدف، (2) تصنيف الإطار و (3) وصف الدور الدليمي.ترتبط المهارات الفرعية الثلاثة ارتباطا وثيقا أثناء وجود دراسات سابقة طرازها بشكل فردي، مما يتجاهل اتصالاتهم المتدربين، وفي الوقت نفسه يحث مشكلة نشر الأخطاء.في هذا العمل، نقترح نموذج عصبي نهاية إلى نهائي لمعالجة المهمة بشكل مشترك.بشكل ملموس، استغلنا طريقة قائمة على الرسم البياني، فيما يتعلق بتحليل الإطار الدلالي كمشكلة بناء الرسم البياني.يتم التعامل مع جميع المسندات والأدوار كجزء رسم بياني، ويتم أخذ علاقاتهم كحواف رسم بياني.نتائج التجربة على مجموعة بيانات قياسية من الإطار الدلالي تظهر أن طريقتنا تنافسية للغاية، مما يؤدي إلى أداء أفضل من نماذج خطوط الأنابيب.
Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently. The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labeling. The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem. In this work, we propose an end-to-end neural model to tackle the task jointly. Concretely, we exploit a graph-based method, regarding frame semantic parsing as a graph construction problem. All predicates and roles are treated as graph nodes, and their relations are taken as graph edges. Experiment results on two benchmark datasets of frame semantic parsing show that our method is highly competitive, resulting in better performance than pipeline models.
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