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Register a new userHyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning
Hyperexpan: توسيع التصنيف مع التعلم التمثيل القطعي
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تعد التصنيفات موارد قيمة للعديد من التطبيقات، ولكن التغطية المحدودة بسبب عملية العمالة اليدوية باهظة الثمن تعوق إمكانية تطبيقها العام. محاولة Works السابقة لتوسيع تصنيفات الأدتصات الموجودة تلقائيا لتحسين تغطيتها من خلال تضمين التعلم بمشاركة مفهوم في الفضاء الإقليدية، في حين أن التصنيفات، التسلسل الهرمي بطبيعتها، محاذاة بشكل طبيعي مع الخصائص الهندسية للفضاء القطعي. في هذه الورقة، نقدم HyperExpan، خوارزمية توسيع تصنيفية تسعى إلى الحفاظ على هيكل التصنيف في مساحة أكثر تعبيرا معبرة وتتعلم أن تمثل المفاهيم وعلاقاتها مع شبكة عصبية خاطئة (HGNN). على وجه التحديد، ترفع Hyperexpan تضمينات الموضع لاستغلال هيكل التصنيفات الموجودة، وتميز معلومات ملف تعريف المفهوم لدعم الاستدلال على مفاهيم جديدة غير مرئية أثناء التدريب. تشير التجارب إلى أن Hyperexpan المقترح تفوق النماذج الأساسية بنماذج أساسية مع التعلم التمثيلي في مساحة ميزة Euclidean وتحقق أداء حديثة على معايير التوسع التصنيفية.
Taxonomies are valuable resources for many applications, but the limited coverage due to the expensive manual curation process hinders their general applicability. Prior works attempt to automatically expand existing taxonomies to improve their coverage by learning concept embeddings in Euclidean space, while taxonomies, inherently hierarchical, more naturally align with the geometric properties of a hyperbolic space. In this paper, we present HyperExpan, a taxonomy expansion algorithm that seeks to preserve the structure of a taxonomy in a more expressive hyperbolic embedding space and learn to represent concepts and their relations with a Hyperbolic Graph Neural Network (HGNN). Specifically, HyperExpan leverages position embeddings to exploit the structure of the existing taxonomies, and characterizes the concept profile information to support the inference on new concepts that are unseen during training. Experiments show that our proposed HyperExpan outperforms baseline models with representation learning in a Euclidean feature space and achieves state-of-the-art performance on the taxonomy expansion benchmarks.
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