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Register a new userOn the Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles
حول استخدام السياق للتنبؤ بالجدارة من الأحكام في المقالات العلمية
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في هذه الورقة، ندرس أهمية السياق في التنبؤ بالجدارة من الجمل في المقالات العلمية.نحن صياغة هذه المشكلة كملمس تسلسل تسلسل باستخدام نموذج Bilstm هرمي.نحن نساهم في مجموعة بيانات قياسية جديدة تحتوي على أكثر من مليوني جمل وملمياتها المقابلة.نحافظ على ترتيب الجملة في هذه البيانات وأداء انقسامات قطار / اختبار على مستوى المستند، والتي تتيح الأهم من دمج المعلومات السياقية في عملية النمذجة.نحن نقيم النهج المقترح على ثلاثة مجموعات من مجموعات البيانات القياسية.تؤدي نتائجنا إلى تحديد فوائد استخدام السياق ومشروع السياق للجدارة.وأخيرا، من خلال تحليل الأخطاء، نحن نقدم رؤى في الحالات التي يلعب فيها السياق دورا أساسيا في التنبؤ بالجدر على الاقتباس.
In this paper, we study the importance of context in predicting the citation worthiness of sentences in scholarly articles. We formulate this problem as a sequence labeling task solved using a hierarchical BiLSTM model. We contribute a new benchmark dataset containing over two million sentences and their corresponding labels. We preserve the sentence order in this dataset and perform document-level train/test splits, which importantly allows incorporating contextual information in the modeling process. We evaluate the proposed approach on three benchmark datasets. Our results quantify the benefits of using context and contextual embeddings for citation worthiness. Lastly, through error analysis, we provide insights into cases where context plays an essential role in predicting citation worthiness.
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