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Register a new userA Review on Document Information Extraction Approaches
مراجعة حول نهج استخراج معلومات المستند
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أصبح استخراج المعلومات من المستندات استخداما رائعا لمناطق معالجة اللغة الطبيعية الجديدة. معظم منهجيات استخراج الكيانات متغير في سياق مثل المنطقة الطبية، المنطقة المالية، كما تقتصر على اللغة المحددة. من الأفضل أن يكون لديك نهج عام واحد ينطبق على أي نوع مستند لاستخراج معلومات الكيان بغض النظر عن اللغة والسياق والهيكل. أيضا، هناك مشكلة أخرى في هذا البحث تحليل هيكلي مع الحفاظ على ميزات التسلسل الهرمي والدلي والمثير. مشكلة أخرى تم تحديدها هي أنه عادة، فإنه يتطلب كوربا تدريب ضخما. لذلك، يركز هذا البحث على التخفيف من هذه الحواجز. تم تحديد العديد من النهج نحو بناء مستخرجات معلومات المستندات التي تركز على تخصصات مختلفة. تتضمن منطقة البحث هذه معالجة اللغة الطبيعية والتحليل الدلالي واستخراج المعلومات والنمذجة المفاهيمية. تقدم هذه الورقة مراجعة لآلية استخراج المعلومات لبناء إطار عام لاستخراج المستندات بهدف توفير قاعدة صلبة للبحث القادم.
Information extraction from documents has become great use of novel natural language processing areas. Most of the entity extraction methodologies are variant in a context such as medical area, financial area, also come even limited to the given language. It is better to have one generic approach applicable for any document type to extract entity information regardless of language, context, and structure. Also, another issue in such research is structural analysis while keeping the hierarchical, semantic, and heuristic features. Another problem identified is that usually, it requires a massive training corpus. Therefore, this research focus on mitigating such barriers. Several approaches have been identifying towards building document information extractors focusing on different disciplines. This research area involves natural language processing, semantic analysis, information extraction, and conceptual modelling. This paper presents a review of the information extraction mechanism to construct a generic framework for document extraction with aim of providing a solid base for upcoming research.
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