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Register a new userDuluth at SemEval-2021 Task 11: Applying DeBERTa to Contributing Sentence Selection and Dependency Parsing for Entity Extraction
Duluth في مهمة Semeval-2021 11: تطبيق Deberta للمساهمة باختيار الجملة وتحليل التبعية لاستخراج الكيان
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تصف هذه الورقة نظام Duluth الذي شارك في مهمة Semeval-2021 11، الرسم البياني للمساهمة NLP.وتفصل في استخراج جمل المساهمة والكيانات العلمية وعلاقاتها من المقالات العلمية في مجال معالجة اللغة الطبيعية.يستخدم حلنا Deberta لتصنيف الجملة المتعدد الفوضى لاستخراج الجمل المساهمة ونوعها، وتحليل التبعية لتحديد كل جملة واستخراج ثلاثة أضعاف ثلاثة أضعاف.احتل نظامنا في المرتبة الخامسة من السبعة للمرحلة الأولى: خط أنابيب نهاية إلى نهاية، السادس من ثمانية للمرحلة 2 الجزء الأول: العبارات والثمانية، والخامس الثمانية للمرحلة 2 الجزء 2: استخراج ثلاثي.
This paper describes the Duluth system that participated in SemEval-2021 Task 11, NLP Contribution Graph. It details the extraction of contribution sentences and scientific entities and their relations from scholarly articles in the domain of Natural Language Processing. Our solution uses deBERTa for multi-class sentence classification to extract the contributing sentences and their type, and dependency parsing to outline each sentence and extract subject-predicate-object triples. Our system ranked fifth of seven for Phase 1: end-to-end pipeline, sixth of eight for Phase 2 Part 1: phrases and triples, and fifth of eight for Phase 2 Part 2: triples extraction.
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This paper describes the winning system in the End-to-end Pipeline phase for the NLPContributionGraph task. The system is composed of three BERT-based models and the three models are used to extract sentences, entities and triples respectively. Exper
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