论文摘要学术规范自动检测模型研究初探
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摘要
的学术规范是衡量学术水平的一个重要方面。目前对论文摘要学术规范的评价研究一般是定性的专家主观性评价,需耗费大量的时间和人力成本,需深入开展对其定量化智能化评价研究。文章借助于机器学习技术,通过对样本数据进行训练,初步构建了一个以论文摘要为研究对象的学术规范自动化检测模型,从而可以实现对学术规范的批量智能化检测,也证明了机器学习技术可用于学术规范的智能化检测的可行性。这对提高科研人员的学术规范水平和降低论文学术规范评价成本有重要意义。
The academic norms of abstracts are an important aspect of measuring academic standards. At present, the evaluation of the academic norms of abstracts is generally a subjective qualitative evaluation of experts, which requires a lot of time and labor costs. Further research on quantitative and intelligent evaluation needs to be studied. Aiming at this problem, the article uses machine learning technology to train the sample data, and initially constructs an academic norm automatic detection model based on the abstract of the paper, which can realize the batch intelligent detection of academic norms, and also prove the feasibility that machine learning technology can be used for the intelligent detection of academic norms. This is of great significance to improve the academic norms of researchers and reduce the cost of academic evaluation of papers.
The academic norms of abstracts are an important aspect of measuring academic standards. At present, the evaluation of the academic norms of abstracts is generally a subjective qualitative evaluation of experts, which requires a lot of time and labor costs. Further research on quantitative and intelligent evaluation needs to be studied. Aiming at this problem, the article uses machine learning technology to train the sample data, and initially constructs an academic norm automatic detection model based on the abstract of the paper, which can realize the batch intelligent detection of academic norms, and also prove the feasibility that machine learning technology can be used for the intelligent detection of academic norms. This is of great significance to improve the academic norms of researchers and reduce the cost of academic evaluation of papers.
引文
1.刘大可,刘学银.基于师生关系的研究生学术规范意识培养机制研究[J].智库时代,2017(13):60-62.
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9.Krizhevsky A,Sutskever I,Hinton G E.ImageNet classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems.Curran Associates Inc,2012:1097-1105.
10.Kim Y.Convolutional Neural Networks for Sentence Classification[J].Eprint Arxiv,2014.
11.Kalchbrenner N,Grefenstette E,Blunsom P.A Convolutional Neural Network for Modelling Sentences[J].Eprint Arxiv,2014.
12.Glorot X,Bengio Y.Understanding the difficulty of training deep feedforward neural networks[J].Journal of Machine Learning Research,2010,9:249-256.
2.王刚.社会科学研究的学术规范[EB/OL].[2017-06-28].http://www.cssn.cn/xspj/xspj/201706/t20170628_3562905.shtml.
3.叶继元.基于质量与创新测度的人文社会科学“全评价”体系及其应用[EB/OL].[2011-11-25].http://www.npopss-cn.gov.cn/GB/219470/16385687.html.
4.全国文献工作标准化技术委员会第六分委员会.GB6447-86文摘编写规则[M].北京:中国标准出版社,1986.
5.高建群,吴玲,施业.学术论文摘要的规范表达[J].东南大学学报(哲学社会科学版),2003,5(2):114-117.
6.金丹,王华菊,李洁,等.从《EI》收录谈科技论文英文摘要的规范化写作[J].编辑学报,2014(s1):118-120.
7.Mihalcea R.TextRank:Bringing order into texts[J].Emnlp,2004:404-411.
8.Hubel DH,Wiesel TN.Receptive fields,binocular interaction and functional architecture in the cat’s visual cortex[J].Journal of Physiology,1962,160(1):106-154.
9.Krizhevsky A,Sutskever I,Hinton G E.ImageNet classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems.Curran Associates Inc,2012:1097-1105.
10.Kim Y.Convolutional Neural Networks for Sentence Classification[J].Eprint Arxiv,2014.
11.Kalchbrenner N,Grefenstette E,Blunsom P.A Convolutional Neural Network for Modelling Sentences[J].Eprint Arxiv,2014.
12.Glorot X,Bengio Y.Understanding the difficulty of training deep feedforward neural networks[J].Journal of Machine Learning Research,2010,9:249-256.