基于论文标题的学科研究主题动力学分析  

Dynamical Analysis of Discipline Research Topics Based on Articles' Titles

作　　者：刘海燕[1] 张志毅 尹晓虎[3] LIU Hai-yan;ZHANG Zhi-yi;YIN Xiao-hu(Party School of the CPC Ji'nan Municipal Party committee, Ji'nan 250100, China;Army Support Department in Northern War Zone, Ji'nan 250002, China;Unit 72465,Ji'nan 250022,China)

机构地区：[1]中共济南市委党校基础教研部 [2]北部战区陆军保障部 [3]72465部队

出　　处：《情报科学》2019年第4期36-43,136,共9页Information Science

基　　金：国家社会科学基金军事学项目"部队思想政治教育的数理基础研究"(16GJ003-47)

摘　　要：【目的/意义】针对中文学术文献数字化资源不完备、信息数据项可用度低的现状,建立了面向论文标题的学科研究主题动力学建模框架,为开展科学计量、把握相关学科研究主题的演化脉络与发展趋势提供了分析手段。【方法/过程】该框架综合运用了自然语言处理、最小描述长度原理、单词向量表示、无监督聚类与卷积神经网络分类器等技术,解决了常规主题建模方法应用于论文标题时面临的分词精度不够、数据稀疏、主题归属难确定等问题,并以改革开放以来思想政治教育研究论文的标题大数据为例进行了演示计算。【结果/结论】实验计算,验证了方法框架的可行性,揭示了四十年来思想政治教育研究主题的分布和演进,为新时代思想政治教育创新发展提供了基点和靶标。【Purpose/significance】In view of the incomplete digitized resources and the low availability of information data items of Chinese academic literature, a dynamic topics modeling framework oriented to paper titles is established, which provides an analytical means for carrying out scientific metrology and grasping the evolutionary context and development trend of research topics in related disciplines.【Method/process】The framework integrates natural language processing, minimum description length principle, word vector representation, unsupervised clustering and convolution neural network classifier to solve the problems of insufficient precision of word segmentation, sparse data and difficult determination of subject attribution faced by conventional topic modeling methods when applied to the titles of papers. A demonstration is carried out, with the big data of the articles′ titles on ideological and political education since the reform and opening up.【Result/conclusion】The experimental calculation verifies the feasibility of our methodological framework, reveals the distribution and evolution of the research themes of ideological and political education in the past 40 years, and provides the basis and target for the innovative development of ideological and political education in the new era.

关 键 词：思想政治教育研究 主题动力学 最小描述长度 向量表示 卷积神经网络 

分 类 号：G250.2[文化科学—图书馆学]