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Register a new userMS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text
يذكر MS: يذكر الكيان الشحني باستمرار في النص
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تعد إجراءات تخليق علوم المواد مجالا واعدا ل NLP العلمي، حيث يمكن أن توفر النمذجة المناسبة لهذه الوصفات نظرة ثاقبة في طرق جديدة لإنشاء المواد. ومع ذلك، فإن التحدي الأساسي في بناء نماذج استخراج المعلومات لإجراءات توليف علوم المواد هو الحصول على ملصقات دقيقة للمواد والعمليات والكيانات الأخرى في تلك الإجراءات. نقدم إحدى كائنات كيان جديدة تذكر التعليقات التوضيحية أكثر من 595 نصية إجرائية تخليق علوم المواد (157،488 رمزا)، والتي توسع بشكل كبير بيانات التدريب المتاحة لمهمة التعرف على الكيان المسمى. نحن نبحث عن مخزون جديد لملفات جديدة مصممة لتوفير شروح متسقة ونهج شرح جديد يهدف إلى زيادة سرعة اتساق وخبراء المجال. تشير دراسات الاتفاقية المشتركة بين النقدية ونماذج الأساس المدربين على البيانات إلى أن كوربوس يوفر شروحا عالية الجودة من هذه الأنواع. تساعد هذه Corpus في وضع أساس النمذجة عالية الجودة في المستقبل لإجراءات التجميعية.
Material science synthesis procedures are a promising domain for scientific NLP, as proper modeling of these recipes could provide insight into new ways of creating materials. However, a fundamental challenge in building information extraction models for material science synthesis procedures is getting accurate labels for the materials, operations, and other entities of those procedures. We present a new corpus of entity mention annotations over 595 Material Science synthesis procedural texts (157,488 tokens), which greatly expands the training data available for the Named Entity Recognition task. We outline a new label inventory designed to provide consistent annotations and a new annotation approach intended to maximize the consistency and annotation speed of domain experts. Inter-annotator agreement studies and baseline models trained upon the data suggest that the corpus provides high-quality annotations of these mention types. This corpus helps lay a foundation for future high-quality modeling of synthesis procedures.
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