面向期刊选择的学术论文内容分类研究  

Classification of Academic Papers for Periodical Selection

作　　者：王鑫芸 王昊[1,2] 邓三鸿 张宝隆[1,2] Wang Xinyun;Wang Hao;Deng Sanhong;Zhang Baolong(School of Information Management,Nanjing University,Nanjing 210023,China;Jiangsu Key Laboratory of Data Engineering and Knowledge Service,Nanjing 210023,China)

机构地区：[1]南京大学信息管理学院,南京210023 [2]江苏省数据工程与知识服务重点实验室,南京210023

出　　处：《数据分析与知识发现》2020年第7期96-109,共14页Data Analysis and Knowledge Discovery

基　　金：江苏省“六大人才高峰”高层次人才基金项目“多粒度学术对象区分性测度和分析研究”(项目编号:JY-001);“江苏省青年社科英才”培养计划和“南京大学仲英青年学者”人才培养计划资助的研究成果之一。

摘　　要：【目的】根据某一学科期刊的收录内容进行层次体系构建,进而在投稿时依据文章内容与期刊的相似程度给出投稿指导意见的方法。【方法】以图书馆学、情报学学科为例,采用层次聚类构建两层体系结构,分别利用SVM、CNN、RNN三种分类方法进行实验,对比其在不同特征组合下的实验效果,选择最合适的分类算法。为了优化分类准确程度,尝试将内容接近的期刊进行组合。【结果】当实验的特征组合更为丰富且更能反映文献的核心内容时,准确率最高可达81.84%。【局限】在进行层次结构体系构建时,选取的实验数据量较小,存在一定的局限性。【结论】在实验数据充足的条件下,深度学习算法比机器学习算法呈现出更明显的分类优势。将内容接近的期刊进行组合后,分类效果可以得到显著提升。[Objective] We constructed a hierarchical system for papers published by academic journals and proposed submission guidance based on the similarity between articles and journals. [Methods] We studied journals in the field of Library and Information Science and used hierarchical clustering to construct two-layer architecture. Then, we employed SVM, CNN, and RNN to classify these papers. Third, we compared the results of different characteristic combinations, and selected the most suitable algorithm. To optimize the classification results, we combined the journals with similar coverage. [Results] Once the characteristic combinations were more reflective to the article contents, we got the highest accuracy of 81. 84%. [Limitations] The data size needs to be expanded. [Conclusions] The deep learning algorithm does a better job in classification than the machine learning algorithm. Combining journals with similar contents improves the classification results.

关 键 词：期刊类目划分 层次分类模型 深度学习 SSCI期刊 分类优化 

分 类 号：G254[文化科学—图书馆学]