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Diffusion of latent semantic analysis as a research tool: A social network analysis approach

机译:潜在语义分析作为研究工具的传播:一种社交网络分析方法

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摘要

Latent semantic analysis (LSA) is a relatively new research tool with a wide range of applications in different fields ranging from discourse analysis to cognitive science, from information retrieval to machine learning and so on. In this paper, we chart the development and diffusion of LSA as a research tool using social network analysis (SNA) approach that reveals the social structure of a discipline in terms of collaboration among scientists. Using Thomson Reuters' Web of Science (WoS), we identified 65 papers with "latent semantic analysis" in their titles and 250 papers in their topics (but not in titles) between 1990 and 2008. We then analyzed those papers using bibliometric and SNA techniques such as co-authorship and cluster analysis. It appears that as the emphasis moves from the research tool (LSA) itself to its applications in different fields, citations to papers with LSA in their titles tend to decrease. The productivity of authors fits Lotka's Law while the network of authors is quite loose. Networks of journals cited in papers with LSA in their titles and topics are well connected.
机译:潜在语义分析(LSA)是一种相对较新的研究工具,在从话语分析到认知科学,从信息检索到机器学习等各个领域中都有广泛的应用。在本文中,我们使用社会网络分析(SNA)方法绘制了LSA作为研究工具的发展和扩散图表,该方法通过科学家之间的协作揭示了一门学科的社会结构。使用汤森路透的Web of Science(WoS),我们在1990年至2008年之间确定了65篇标题中带有“潜在语义分析”的论文,以及250篇主题中的论文(但没有标题)。然后,我们使用文献计量法和SNA分析了这些论文。共同作者和聚类分析等技术。看起来,随着重点从研究工具(LSA)本身转移到其在不同领域中的应用,对带有标题为LSA的论文的引用被减少。作者的生产力符合洛特卡定律,而作者的网络却很松散。具有LSA标题和主题的论文所引用的期刊网络之间的联系紧密。

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