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Register a new userHiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition
HITRANS: شبكة محول هرمية للتعرف على الكيان المسمى المتداخل
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تم دراسة التعرف على الكيان المسمى Nestate (NNER) على نطاق واسع، تهدف إلى تحديد جميع الكيانات المتداخلة من تمديدات محتملة (I.E.، واحد أو أكثر من الرموز المستمرة). ومع ذلك، فإن الدراسات الحديثة لأي نانر إما التركيز على مخططات العلامات الشاقة أو الاستفادة من الهياكل المعقدة، والتي تفشل في تعلم تمثيلات فعالة من جملة المدخلات مع كيانات متداخلة للغاية. بمعنى حدسي، ستساهم تمثيلات صريحة في نانر بسبب معلومات السياق الغنية التي تحتوي عليها. في هذه الدراسة، نقترح شبكة محول هرمية (HITRANS) للمهمة NNER، والتي تتحلل جملة الإدخال إلى تمثال متعدد الحبوب وتعزز التعلم التمثيل بطريقة هرمية. على وجه التحديد، نستخدم أول وحدة من المرحلة الأولى لتوليد تمثيلات تمتد عن طريق معلومات السياق التجميعية بناء على شبكة محول من أسفل إلى أعلى وهبوطا. ثم تم تصميم طبقة التنبؤ الملصق للتعرف على الكيانات المتداخلة هرمية، والتي تستكشف بشكل طبيعي التبعيات الدلالية بين تمديد مختلفة. تثبت تجارب مجموعات بيانات Genia و ACE-2004 و ACE-2005 و NNE أن طريقةنا المقترحة تحقق أداء أفضل بكثير من النهج التي من بين الفني.
Nested Named Entity Recognition (NNER) has been extensively studied, aiming to identify all nested entities from potential spans (i.e., one or more continuous tokens). However, recent studies for NNER either focus on tedious tagging schemas or utilize complex structures, which fail to learn effective span representations from the input sentence with highly nested entities. Intuitively, explicit span representations will contribute to NNER due to the rich context information they contain. In this study, we propose a Hierarchical Transformer (HiTRANS) network for the NNER task, which decomposes the input sentence into multi-grained spans and enhances the representation learning in a hierarchical manner. Specifically, we first utilize a two-phase module to generate span representations by aggregating context information based on a bottom-up and top-down transformer network. Then a label prediction layer is designed to recognize nested entities hierarchically, which naturally explores semantic dependencies among different spans. Experiments on GENIA, ACE-2004, ACE-2005 and NNE datasets demonstrate that our proposed method achieves much better performance than the state-of-the-art approaches.
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