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Register a new userActive Learning for Interactive Relation Extraction in a French Newspaper's Articles
التعلم النشط لاستخراج العلاقات التفاعلية في مقالات الصحف الفرنسية
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استخراج العلاقات هو الترجمة الفرعية لمعالجة Langage الطبيعية التي شهدت العديد من التحسينات في السنوات الأخيرة، مع ظهور البنية المعقدة المدربة مسبقا. يتم اختبار العديد من هذه النهج من هذه النهج من المعايير مع الجمل المسماة التي تحتوي على كيانات الموسومة، وتتطلب التدريب المسبق الهامة والضبط بشكل جيد على البيانات الخاصة بالمهام. ومع ذلك، في سيناريو حقيقي للاستخدام، مثل في شركة صحيفة في الغالب مخصصة لمعلومات المحلية، فإن العلاقات هي من نوع متنوع للغاية، مع عدم وجود بيانات مشروح تقريبا لمثل هذه العلاقات، والعديد من الكيانات تعاني في جملة دون أن تكون ذات صلة. نشكك في استخدام النماذج الإشرفة من أحدث النماذج في هذا السياق، حيث توجد موارد مثل الوقت والحوسبة وقوة الحوسبة والنحاذج البشرية محدودة. للتكيف مع هذه القيود، نقوم بتجربة خط أنابيب استخراج التعلم في التعلم النشط، وتتألف من نموذج خفيف الوزن يستند إلى LSTM ثنائي للكشف عن العلاقات الموجودة، ونموذج أحدث لتصنيف العلاقة. قارن العديد من الخيارات لنماذج التصنيف في هذا السيناريو، من الكلمة الأساسية لتضمين المتوسط، على الرسم البياني للشبكات العصبية وتلك القائمة على برت، وكذلك العديد من استراتيجيات الاستحواذ النشطة للتعلم، من أجل إيجاد نهج الأكثر كفاءة من حيث التكلفة ولكن دقيقة في موقعنا أكبر حالة استخدام شركة صحيفة صحيفة الفرنسية.
Relation extraction is a subtask of natural langage processing that has seen many improvements in recent years, with the advent of complex pre-trained architectures. Many of these state-of-the-art approaches are tested against benchmarks with labelled sentences containing tagged entities, and require important pre-training and fine-tuning on task-specific data. However, in a real use-case scenario such as in a newspaper company mostly dedicated to local information, relations are of varied, highly specific type, with virtually no annotated data for such relations, and many entities co-occur in a sentence without being related. We question the use of supervised state-of-the-art models in such a context, where resources such as time, computing power and human annotators are limited. To adapt to these constraints, we experiment with an active-learning based relation extraction pipeline, consisting of a binary LSTM-based lightweight model for detecting the relations that do exist, and a state-of-the-art model for relation classification. We compare several choices for classification models in this scenario, from basic word embedding averaging, to graph neural networks and Bert-based ones, as well as several active learning acquisition strategies, in order to find the most cost-efficient yet accurate approach in our French largest daily newspaper company's use case.
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https://aclanthology.org/
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