Natural Language Process: A New Kind of Nuclear Quality Assurance Management Tool
| Autor: | Zeyu Wang, Yan Sun, Yongqing Guan, Qiyuan Zheng |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Process (engineering)
business.industry Event (computing) Computer science Unstructured data 02 engineering and technology computer.software_genre Data science Automatic summarization Domain (software engineering) Named-entity recognition 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Quality assurance Natural language processing Natural language |
| Zdroj: | Energy Procedia. 127:201-219 |
| ISSN: | 1876-6102 |
| DOI: | 10.1016/j.egypro.2017.08.096 |
| Popis: | Quality Assurance Management is imperative in nuclear engineering. And quality assurance language is a rigorous and highly logical language in workplaces. We build machine-learning models of natural language process about quality assurance management activities to extract valuable information from texts for management intelligently. As technological means, the primary purpose of NLP tools here is that converting massive unstructured data (text) to structured data (data attribute relationship).The tasks include event classification, named entity recognition (NER) of engineering, event domain judgment, automatic summarization and event similarity computing. We focus on Labeled-LDA and SVMs algorithms to perform short text classification. And using them as primary content, we can perform more advanced nuclear quality assurance management in future. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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