تصف هذه الورقة النظام الذي بنناه كفريق YNU-HPCC في مهمة Semeval-2021 11: NLPContribeGraph. تتضمن هذه المهمة أولا تحديد الجمل في المقالات العلمية المعينة للغة الطبيعية (NLP) التي تعكس مساهمات البحث من خلال التصنيف الثنائي؛ ثم تحديد المصطلحات العلمية الأساسية وعبارات علاقتها من جمل هذه المساهمة عن طريق وضع التسلسل؛ وأخيرا، يتم تصنيف هذه المصطلحات والعلاقات العلمية هذه، وحددها، ويتم تنظيمها في ثلاثة أضعاف ثلاثة أضعاف لتشكيل رسم بياني للمعرفة بمساعدة تصنيف Multiclass وتصنيف متعدد التسميات. قمنا بتطوير نظام لهذه المهمة باستخدام نموذج تمثيل لغوي مدرب مسبقا يسمى Bert الذي يمثل تمثيلات تشفير ثنائية الاتجاه من المحولات، وحقق نتائج جيدة. متوسط درجة F1 للتقييم المرحلة 2، الجزء الأول كان 0.4562 واحتل المرتبة 7، ومتوسط درجة F1 لمرحلة التقييم 2، الجزء الثاني كان 0.6541، وأيضا المرتبة 7.
This paper describes the system we built as the YNU-HPCC team in the SemEval-2021 Task 11: NLPContributionGraph. This task involves first identifying sentences in the given natural language processing (NLP) scholarly articles that reflect research contributions through binary classification; then identifying the core scientific terms and their relation phrases from these contribution sentences by sequence labeling; and finally, these scientific terms and relation phrases are categorized, identified, and organized into subject-predicate-object triples to form a knowledge graph with the help of multiclass classification and multi-label classification. We developed a system for this task using a pre-trained language representation model called BERT that stands for Bidirectional Encoder Representations from Transformers, and achieved good results. The average F1-score for Evaluation Phase 2, Part 1 was 0.4562 and ranked 7th, and the average F1-score for Evaluation Phase 2, Part 2 was 0.6541, and also ranked 7th.
References used
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