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Register a new userTransfer-based Enrichment of a Hungarian Named Entity Dataset
التخصيب القائم على النقل لمجموعة بيانات كيكة مجرية
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في هذه الورقة، نقدم تحديثا كبيرا إلى أول بيانات كيكة مجرية مسماة، The Szeged Ner Corpus.استخدمنا النقل الصفر - النار عبر اللغات لتهيئة تخصيب أنواع الكيان المشروح في الجسر باستخدام ثلاث نماذج NER العصبية: اثنان منها بناء على Corpus English Ontonotes و One استنادا إلى Czech Cority Corpus Corpus Finetuned من نماذج اللغة العصبية متعددة اللغاتوبعدتم دمج إخراج النماذج تلقائيا مع التوضيحية الأصلية NER، وتصحيحها تلقائيا وتوجه المزيد من التوضيح الإضافي، مثل التصفيات اللازمة لأنواع الكيانات المختلفة.نقدم تقييم الأداء الصفر بالرصاص للنماذج التي تستند إلى OnTonotes ونموذج NEM جديد يستند إلى المحولات التي تم تدريبها على الجزء التدريبي من The Final Corpus.نحن نفرج عن كوربوس والنموذج المدربين.
In this paper, we present a major update to the first Hungarian named entity dataset, the Szeged NER corpus. We used zero-shot cross-lingual transfer to initialize the enrichment of entity types annotated in the corpus using three neural NER models: two of them based on the English OntoNotes corpus and one based on the Czech Named Entity Corpus finetuned from multilingual neural language models. The output of the models was automatically merged with the original NER annotation, and automatically and manually corrected and further enriched with additional annotation, like qualifiers for various entity types. We present the evaluation of the zero-shot performance of the two OntoNotes-based models and a transformer-based new NER model trained on the training part of the final corpus. We release the corpus and the trained model.
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