多特征融合的电气领域主观题自动评分方法

Automatic Scoring Method of Subjective Questions in Electrical Field Based on Multi-Feature Fusion

智慧教育运用人工智能等技术来实现教育的智能化,将智慧教育应用到电气类高校教学、注册电气工程师考试和电网员工入职培训等领域,有助于学生和工作人员快速掌握电气专业知识。基于电气领域本体和自然语言处理技术,探索了电气领域主观题自动评分方法。针对通用词语相似度计算存在的不足,设计了一种融合《同义词词林》和《知网》的词语相似度方法,并结合文本的表层结构信息以及语义信息,提出了一种结合句子的词序、词形和搭配词对三种特征的电气领域主观题自动评分方法。试验表明,该方法能够有效解决传统评分算法对专业术语的识别不全面和未考虑文本中句子的语义关系等问题,有效地提升了评分结果的准确度。

Smart Education employs artificial intelligence and other technologies to realize the intelligence of education,and applies smart education to the fields of teaching in electrical colleges and universities,registered electrical engineer examinations and power grid staff induction training,which helps students and staff to quickly master electrical expertise.Based on electrical domain ontology and natural language processing technology,an automatic scoring method for subjective questions in electrical domain was explored.In view of the shortcomings of generic word similarity calculation,a word similarity method integrating synonym word forest and knowledge network was designed,and an automatic scoring method combining three features of word order,word form and collocated word pairs in electrical domain was proposed by combining the superficial structure information and semantic information of the text.The experimental results show that the proposed method can effectively solve the problems of incomplete recognition of specialized terms and failure to consider the semantic relationship of sentences in the text by the traditional scoring algorithm,and effectively improve the accuracy of scoring results.