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Register a new userFine-grained Entity Typing via Label Reasoning
كيان غرامة الكتابة عن طريق تسمية التسمية
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تستند نهج كتابة الكيانات التقليدية إلى نماذج تصنيف مستقلة، مما يجعلها من الصعب التعرف على أنواع الكيان المعتمدة والذات طويل الذيل والحبوب. في هذه الورقة، نجادل بأن التبعيات الخارجية والمترنوية المتطودة ضمنيا بين الملصقات يمكن أن توفر معرفة حاسمة لمعالجة التحديات المذكورة أعلاه. تحقيقا لهذه الغاية، نقترح شبكة تسمية الملصقات (LRN)، والتي الأسباب التسلل تسميات الكيانات الجميلة من خلال اكتشاف واستغلال المعرفة التبعيات الملصقات المتاحة في البيانات. على وجه التحديد، تستخدم LRN شبكة التراجع التلقائي لإجراء منطق استنتاجي ورسم رسمي من السمة الحيوية لإجراء منطق حثي بين الملصقات، والتي يمكن أن تكفذ بشكل فعال، وتعلم وسبب تبعيات التسمية المعقدة في مجموعة تسلسل إلى مجموعة، ونهاية إلى نهاية وبعد تظهر التجارب أن LRN تحقق الأداء الحديثة على معايير كيانات كيانات فائقة الفيرلة القياسية، ويمكن أيضا حل مشكلة تسمية الذيل الطويل بشكل فعال.
Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types. In this paper, we argue that the implicitly entailed extrinsic and intrinsic dependencies between labels can provide critical knowledge to tackle the above challenges. To this end, we propose Label Reasoning Network(LRN), which sequentially reasons fine-grained entity labels by discovering and exploiting label dependencies knowledge entailed in the data. Specifically, LRN utilizes an auto-regressive network to conduct deductive reasoning and a bipartite attribute graph to conduct inductive reasoning between labels, which can effectively model, learn and reason complex label dependencies in a sequence-to-set, end-to-end manner. Experiments show that LRN achieves the state-of-the-art performance on standard ultra fine-grained entity typing benchmarks, and can also resolve the long tail label problem effectively.
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Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision. However, the reliance on KB means this training setting can be hampered by the lack of
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Despite the success of neural dialogue systems in achieving high performance on the leader-board, they cannot meet users' requirements in practice, due to their poor reasoning skills. The underlying reason is that most neural dialogue models only cap
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