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Register a new userA Partition Filter Network for Joint Entity and Relation Extraction
شبكة تصفية القسم لاستخراج الكيان المشترك والعلاقة
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في استخراج الكيان المشترك والعلاقة، العمل الحالي إما ترميز الميزات الخاصة بمهام المهام بالتتابع، مما يؤدي إلى عدم التوازن في تفاعل الميزات المشتركة بين المهام حيث لا يكون للميزات المستخرجة لاحقا اتصالا مباشرا مع تلك التي تأتي أولا. أو ترميز ميزات الكيان وميزات العلاقة بطريقة متوازية، مما يعني أن التعلم التمثيل الميزات لكل مهمة مستقلة إلى حد كبير عن بعضها البعض باستثناء مشاركة الإدخال. نقترح شبكة تصفية القسم لنموذج التفاعل في اتجاهين بين المهام بشكل صحيح، حيث تحلل ترميز الميزة في خطوتين: القسم والتصفية. في تشفيرنا، نحن نستفيد بوابات اثنين: كيان وبوابة العلاقة، إلى الخلايا العصبية بالقطاع إلى قسمين مهمتين وتقسيم مشترك واحد. يمثل القسم المشترك معلومات مشتركة بين المهام القيمة لكل من المهام ويتم تقاسمها بالتساوي عبر مهمتين لضمان التفاعل السليم في اتجاهين. تمثل أقسام المهام معلومات مهمة داخلية ويتم تشكيلها من خلال الجهود المتضاحية لكل من البوابات، مما يتأكد من أن ترميز ميزات المهام الخاصة يعتمد على بعضها البعض. تظهر نتائج التجربة على ستة مجموعات بيانات عامة أن طرازنا يؤدي أفضل بكثير من النهج السابقة. بالإضافة إلى ذلك، على عكس ما ادعى العمل السابق، تشير تجاربنا الإضافية إلى أن التنبؤ بالعلامة مساهمة في تنبؤ الكيان المسمى بطريقة غير مهم. يمكن العثور على شفرة المصدر في https://github.com/coopercoper/pfn.
In joint entity and relation extraction, existing work either sequentially encode task-specific features, leading to an imbalance in inter-task feature interaction where features extracted later have no direct contact with those that come first. Or they encode entity features and relation features in a parallel manner, meaning that feature representation learning for each task is largely independent of each other except for input sharing. We propose a partition filter network to model two-way interaction between tasks properly, where feature encoding is decomposed into two steps: partition and filter. In our encoder, we leverage two gates: entity and relation gate, to segment neurons into two task partitions and one shared partition. The shared partition represents inter-task information valuable to both tasks and is evenly shared across two tasks to ensure proper two-way interaction. The task partitions represent intra-task information and are formed through concerted efforts of both gates, making sure that encoding of task-specific features is dependent upon each other. Experiment results on six public datasets show that our model performs significantly better than previous approaches. In addition, contrary to what previous work has claimed, our auxiliary experiments suggest that relation prediction is contributory to named entity prediction in a non-negligible way. The source code can be found at https://github.com/Coopercoppers/PFN.
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Joint entity and relation extraction is challenging due to the complex interaction of interaction between named entity recognition and relation extraction. Although most existing works tend to jointly train these two tasks through a shared network, t
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