基于语义相似度及命名实体识别的主观题自动评分方法
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摘要
人工智能技术目前已运用于诸多领域,其中在教育行业的一个重要应用为主观题评分系统。文本相似度计算是主观题评分的一大难点,目前采用的基于同义词词林的词语相似度计算方法已经了取得较好的效果,但文本过长会导致传统词语相似度计算方法性能下降。该文采用拓展的命名实体识别方法将主观题的候选答案中部分关键词提取出来,采用改进的同义词林词语相似度计算方法将候选关键词与主观题标准答案中目标关键词进行相似度计算。所提方法能有效提升词语匹配效率,在原同义词林词语相似度算法基础上,提升了性能,有效缩短了计算时间。
Artificial intelligence technology has been widely used in many areas.One of the applicationsinthe education industry is automated scoring system for subjective questions.The text similarity calculation is a major difficulty in subjective question scoring.The word similarity calculation based onCiLin has achieved good results,but the long text will lead to the performance degradation of the traditional word similarity calculation method.In this paper,the extended named entity recognition method is used to extract some keywords from the candidate answers of the subjective questions.the improved CiLinword semantic similarity calculation method is used to calculate the similarity between the candidate keywords and the target keywords.The proposed method can effectively improve the word matching efficiency.On the basis of the original CiLinword semantic similarity algorithm,the performance is improved and the calculation time is effectively shortened.
Artificial intelligence technology has been widely used in many areas.One of the applicationsinthe education industry is automated scoring system for subjective questions.The text similarity calculation is a major difficulty in subjective question scoring.The word similarity calculation based onCiLin has achieved good results,but the long text will lead to the performance degradation of the traditional word similarity calculation method.In this paper,the extended named entity recognition method is used to extract some keywords from the candidate answers of the subjective questions.the improved CiLinword semantic similarity calculation method is used to calculate the similarity between the candidate keywords and the target keywords.The proposed method can effectively improve the word matching efficiency.On the basis of the original CiLinword semantic similarity algorithm,the performance is improved and the calculation time is effectively shortened.
引文
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[2] 曹玉婵,左映龙.浅析在线考试主观题智能阅卷算法的设计与实现[J].价值工程,2014,33(17):231-233.
[3] 高思丹,袁春风.语句相似度计算在主观题自动批改技术中的初步应用[J].计算机工程与应用,2004(14):132-135.
[4] 刘逸雪,卢雨轩,丁亮, 等.基于Bi-LSTM的数学主观题自动阅卷方法[J].管理观察,2018(2):109-113.
[5] 张均胜,石崇德,徐红姣, 等.一种基于短文本相似度计算的主观题自动阅卷方法[J].图书情报工作,2014,58(19):31-38.
[6] 李春梅,徐庆生.基于多特征的汉语句子相似度计算模型的研究[J].计算机技术与发展,2014,24(6):136-139,144.
[7] 何晓梅.基于条件随机场的音乐共同语义标注[J].电子测量技术,2016,39(8):70-74.
[8] 田久乐,赵蔚.基于同义词词林的词语相似度计算方法[J].吉林大学学报(信息科学版),2010,28(6):602-608.
[9] 吕立辉,梁维薇,冉蜀阳.基于词林的词语相似度的度量[J].现代计算机(专业版),2013(1):3-6,9.
[10] 朱新华,马润聪,孙柳, 等.基于知网与词林的词语语义相似度计算[J].中文信息学报,2016,30(4):29-36.
[11] CHE W X, LIZ H, LIU T. Ltp: A chinese language technology platform[C]. 23rd International Conference on Computational Linguistics: Demonstrations, Association for Computational Linguistics, 2010.
[12] 张梅山,邓知龙,车万翔, 等.统计与词典相结合的领域自适应中文分词[J].中文信息学报,2012,26(2):8-12.
[13] 王丽杰, 车万翔, 刘挺, 等. 基于SVMTool的中文词性标注[J]. 中文信息学报, 2009, 23(4):16-22.
[14] LTP语言技术平台. 使用训练套件[DB/OL]. http://ltp.ai/docs/train.html.
[15] 梅家驹.同义词词林[M].上海:上海辞书出版社,1996.