基于特征测度和PhraseLDA模型的领域学科交叉主题识别研究——以纳米技术的农业环境应用领域为例

Interdisciplinary Subject Recognition Based on Feature Measurement and PhraseLDA Model——Case Study of Nanotechnology in Agricultural Environment

【目的】基于特征测度方法和PhraseLDA模型,对领域学科交叉主题进行识别。【方法】通过主题的学科交叉特征分析,构建学科交叉主题测度指标体系,结合PhraseLDA模型识别领域学科交叉主题,最后在纳米技术的农业环境应用领域进行实证研究。【结果】客观识别出纳米技术的农业环境应用领域包括催化剂制备、土壤生物修复等交叉主题24个,相较于传统识别方法,交叉主题识别率提升71.40%,细粒度主题识别率提升42.86%。【局限】PhraseLDA主题模型的主题数量和学科交叉主题识别指标等阈值是经过反复计算调试而设定,因此,本文方法对相关阈值设定的合理性存在一定依赖性。【结论】本文方法可有效识别领域中的学科交叉主题,为相关领域开展科学决策和科技创新研究提供辅助参考。

[Objective]This paper aims to identify the interdisciplinary subjects based on the feature measure method and the PhraseLDA model.[Methods]First,we analyzed the subjects’interdisciplinary characteristics and constructed their measurement index system.Then,we identified the interdisciplinary subjects with the help of the PhraseLDA model.Finally,we conducted an empirical study of nanotechnology applications in agricultural environments.[Results]A total of 24 cross-topic were objectively identified,including catalyst preparation,soil bioremediation,and many more.Compared with the traditional identification method,the cross-topic recognition rate of the proposed method increased by 71.40%,and the recognition rate of fine-grained topics increased by 42.86%.[Limitations]The number of topics and interdisciplinary topic identification indicators of the PhraseLDA topic model were decided after repeating calculation and debugging.Therefore,the proposed method depends on the rationality of the relevant thresholds.[Conclusions]The proposed method can effectively identify interdisciplinary topics and support scientific decision-making and technological innovation research in related fields.