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- 2018
基于复合主题演化模型的作者研究兴趣动态发现
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Abstract:
摘要： 以金融领域的科技文献作为实验数据,提出了一种新的用于动态挖掘领域相关的作者研究兴趣的复合主题演化模型。该模型能够获取作者在不同时间片下的主题概率分布以及主题下词汇概率分布,并充分考虑作者在合作作者文献中的排名对于其研究主题和主题变化的影响。通过金融领域的实证研究表明,该复合主题演化模型能够有效地揭示金融领域作者研究兴趣的动态变化。
Abstract: We propose a new combined topic model, i.e. author topic time-latent dirichlet allocation(ATT-LDA)with author ranking(AR), for the of dynamic discovery of researchers' interest, which is based on the academic literature in the financial field. Through the proposed model, we can easily acquire the probability distribution of the authors' interest, as well as the probability distribution of topics on deferent words. The influence of the ranking in the co-author list are fully taken into consideration. The empirical study shows that the proposed method can effectively reveal the dynamic change of interest of the authors in the financial field
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