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Register a new userTransfer Learning for Czech Historical Named Entity Recognition
نقل التعلم لتشيكية التعرف على الكيان الرسمي
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في الوقت الحاضر، حقق التعرف على الكيان المسمى (NER) نتائج ممتازة على الشركة القياسية.ومع ذلك، فإن المشكلات الكبيرة تنشأ مع الحاجة إلى تطبيق في مجال معين، لأنه يتطلب جدارا الشكل المشروح مع مجموعة علامات NE مكيفة.هذا واضح بشكل خاص في مجال معالجة المستندات التاريخية.يتكون الهدف الرئيسي لهذه الورقة من اقتراح وتقييم العديد من طرق تعلم النقل لزيادة درجة النقر التاريخي التشيكي.ندرس العديد من مصادر المعلومات، ونحن نستخدم شباكين عصبيين للنمذجة والاعتراف.نحن نوظف سورانيا لتقييم أساليب التعلم الخاصة بنا، وهي Czech Named Entity Corpus و Czech Historical Enty Enty Engyity Corpus.نظهر أن تمثيل بيرت بضبط جيد وفقط المصنف البسيط المدرب على اتحاد كورسيا يحقق نتائج ممتازة.
Nowadays, named entity recognition (NER) achieved excellent results on the standard corpora. However, big issues are emerging with a need for an application in a specific domain, because it requires a suitable annotated corpus with adapted NE tag-set. This is particularly evident in the historical document processing field. The main goal of this paper consists of proposing and evaluation of several transfer learning methods to increase the score of the Czech historical NER. We study several information sources, and we use two neural nets for NE modeling and recognition. We employ two corpora for evaluation of our transfer learning methods, namely Czech named entity corpus and Czech historical named entity corpus. We show that BERT representation with fine-tuning and only the simple classifier trained on the union of corpora achieves excellent results.
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