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Register a new userEvent Prominence Extraction Combining a Knowledge-Based Syntactic Parser and a BERT Classifier for Dutch
استخراج الحدث بالبروز يجمع بين محلل نحانوني مقرها المعرفة ومصنف بيرت للهولندية
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المهمة الأساسية في استخراج المعلومات هي اكتشاف الحدث الذي يحدد مشغلات الحدث في الجمل التي يتم تصنيفها عادة في أنواع الأحداث. في هذه الدراسة، يعتبر الحدث وحدة لقياس التنوع والتشابه في مقالات إخبارية في إطار نظام أخبار التوصية. فشلت نهج اكتشاف الحدث المستندة إلى التصنيف الحالي في التعامل مع مجموعة متنوعة من الأحداث المعبر عنها في مواقف العالم الحقيقي. للتغلب على ذلك، نهدف إلى أداء تصنيف حفلات الأحداث واستكشاف ما إذا كان نموذج محول قادر على تصنيف معلومات جديدة في فصول بروز أقل وأكثر عمومية. بعد مقارنة خط الأساس من آلة ناقلات الدعم (SVM) وعروض التصنيف القائم على المحولات لدينا في العديد من تنسيقات سبين الأحداث، فقد تم تصميمنا حدث متعدد الكلام يمتد كشروط سليمة. يتم تغذية تلك الموجودة في تصنيفنا البرز الذي يتم ضبطه بشكل جيد على Adgeddings الهولندية المدربة مسبقا. علاوة على ذلك، نحن نتفوق على خط أنابيب لنهج حقل عشوائي مشروط (CRF) في اكتشاف كلمة الزناد في الأحداث والتصنيف المستند إلى BERT. إلى حد ما من معرفتنا، نقدم أول نهج استخراج الأحداث الذي يجمع بين محلل نصلي مقصورات مقره الخبراء مع مصنف تحويل محول للهولندية.
A core task in information extraction is event detection that identifies event triggers in sentences that are typically classified into event types. In this study an event is considered as the unit to measure diversity and similarity in news articles in the framework of a news recommendation system. Current typology-based event detection approaches fail to handle the variety of events expressed in real-world situations. To overcome this, we aim to perform event salience classification and explore whether a transformer model is capable of classifying new information into less and more general prominence classes. After comparing a Support Vector Machine (SVM) baseline and our transformer-based classifier performances on several event span formats, we conceived multi-word event spans as syntactic clauses. Those are fed into our prominence classifier which is fine-tuned on pre-trained Dutch BERT word embeddings. On top of that we outperform a pipeline of a Conditional Random Field (CRF) approach to event-trigger word detection and the BERT-based classifier. To the best of our knowledge we present the first event extraction approach that combines an expert-based syntactic parser with a transformer-based classifier for Dutch.
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https://aclanthology.org/
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