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Register a new userFine-grained Entity Typing without Knowledge Base
كيان غرين غرامة الكتابة دون قاعدة المعرفة
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يعمل العمل الحالي على كتابة كيان غرامة (FET) عادة النماذج التلقائية على مجموعات البيانات التي تم الحصول عليها باستخدام قواعد المعرفة (KB) كشراف بعيد.ومع ذلك، فإن الاعتماد على KB يعني أنه يمكن إعاقة هذا الإعداد التدريبي من خلال عدم وجود أو عدم انتظام KB.لتخفيف هذا القيد، نقترح إعدادا جديدا لتدريب نماذج FET: FET دون الوصول إلى أي قاعدة معارف.بموجب هذا الإعداد، نقترح إطار من خطوتين لتدريب نماذج FET.في الخطوة الأولى، نقوم تلقائيا بإنشاء بيانات زائفة مع ملصقات ممتعة من مجموعة بيانات كبيرة غير مسبوقة.ثم يتم تدريب نموذج شبكة عصبي بناء على البيانات الزائفة، إما بطريقة غير مخالفة أو استخدام التدريب الذاتي بموجب الإرشادات الضعيفة من نموذج التعرف على الكيان الحبيبة الخشنة (NER).تظهر النتائج التجريبية أن طريقتنا تحقق أداء تنافسي فيما يتعلق بالنماذج المدربة على مجموعات البيانات ذات الإشراف على KB الأصلي.
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 or the incompleteness of the KB. To alleviate this limitation, we propose a novel setting for training FET models: FET without accessing any knowledge base. Under this setting, we propose a two-step framework to train FET models. In the first step, we automatically create pseudo data with fine-grained labels from a large unlabeled dataset. Then a neural network model is trained based on the pseudo data, either in an unsupervised way or using self-training under the weak guidance from a coarse-grained Named Entity Recognition (NER) model. Experimental results show that our method achieves competitive performance with respect to the models trained on the original KB-supervised datasets.
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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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