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Register a new userSynchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction
شبكة مزدوجة متزامنة مع الاهتمام عبر النوع من أجل كيان مشترك واستخراج العلاقات
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إن كيان مشترك واستخراج العلاقات يمثل تحديا بسبب التفاعل المعقد للتفاعل بين التعرف على الكيان المسمى واستخراج العلاقة. على الرغم من أن معظم الأعمال القائمة تميل إلى تدريب هذه المهامتين المشتركين من خلال شبكة مشتركة، إلا أنها تفشل في الاستفادة الكاملة من الترابط بين أنواع الكيان وأنواع العلاقات. في هذه الورقة، نقوم بتصميم شبكة مزدوجة متزامنة رواية (SDN) مع اهتمام عبر النوع عبر الانتباه بشكل منفصل وتفاعي تفاعلي أنواع الكيان وأنواع العلاقات. من ناحية، يعتمد SDN اثنين من النوع BI اتجاهي ISOMORPHIC LSTM لترميز التمثيلات المحسنة نوع الكيان والتمثيلات المحسنة نوع العلاقة، على التوالي. من ناحية أخرى، نماذج SDN صراحة الترابط بين أنواع الكيان وأنواع العلاقات عبر آلية الاهتمام عبر النوع. بالإضافة إلى ذلك، نقترح أيضا استراتيجية تعليمية متعددة المهام الجديدة عبر النمذجة تفاعل نوعين من المعلومات. تجارب مجموعات بيانات NYT و WEBNLG تحقق من فعالية النموذج المقترح، وتحقيق الأداء الحديث في الفن.
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, they fail to fully utilize the interdependence between entity types and relation types. In this paper, we design a novel synchronous dual network (SDN) with cross-type attention via separately and interactively considering the entity types and relation types. On the one hand, SDN adopts two isomorphic bi-directional type-attention LSTM to encode the entity type enhanced representations and the relation type enhanced representations, respectively. On the other hand, SDN explicitly models the interdependence between entity types and relation types via cross-type attention mechanism. In addition, we also propose a new multi-task learning strategy via modeling the interaction of two types of information. Experiments on NYT and WebNLG datasets verify the effectiveness of the proposed model, achieving state-of-the-art performance.
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https://aclanthology.org/
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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 t
hey 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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tions pose new challenges for learning effective neural models. In this paper, we propose a novel dictionary extension method which extracts new entities through the type expanded model. Moreover, we design a multi-granularity boundary-aware network which detects entity boundaries from both local and global perspectives. We conduct experiments on different types of datasets, the results show that our model outperforms previous state-of-the-art distantly supervised systems and even surpasses the supervised models.
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