学术成果主题新颖性测度方法研究——基于Doc2Vec和HMM算法

Measuring Novelty of Scholarly Articles

【目的】进一步研究和构建学术成果主题新颖性测度指标,量化地测度学术成果的主题新颖性。【方法】构建基于Doc2Vec和HMM的文本内容特征因子计算模型,通过该内容特征因子构造文档的主题新颖性测度指标函数。基于《情报学报》、《情报科学》和《情报杂志》三本期刊2014年度所刊载的学术论文进行实证研究,测度论文的主题新颖性。【结果】将该方法与已有的新颖性测度方法的计算结果进行对比,其结果表现为0.01水平上的显著相关关系,相关系数为0.494,同时该方法也避免了已有方法的计算结果中存在的区分度不够的问题。【局限】仅使用摘要文本进行挖掘,未对全文信息进行深入挖掘。【结论】基于该方法的学术成果主题新颖性测度方法和计算结果具有可操作性和可解释性,可以为学术评价和学科前沿探测提供有效支撑。

[Objective] This paper aims to construct a novelty index to evaluate the academic achievements. [Methods] First, we proposed a model to calculate content eigenfactor based on deep learning（Doc2Vec） and Hidden Markov Model. Then, we built the topic novelty measure index. Finally, we examined the proposed method with academic papers published by three Chinese LIS journals in 2014. [Results] Compared with the existing methods, the proposed model measured the topic novelty more effectively. [Limitations] Our empirical research only examined abstracts of the academic papers. [Conclusions] The proposed method could help us evaluate and monitor scholarly research.