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arXiv 1706.08671cs.DLphysics.soc-ph
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利用文本分析量化科学学科的相似性和演化

Using text analysis to quantify the similarity and evolution of scientific disciplines

Laercio Dias, Martin Gerlach, Joachim Scharloth, Eduardo G. Altmann

摘要:

我们使用信息理论的语言相似性的措施,调查的组织和科学领域的演变。对过去30年近2000万篇论文的分析表明,语言相似性与专家和基于引用的分类有关,但不同,从而改善了对科学组织的看法。场的相似性的时间分析表明,一些场(例如,计算机科学)正变得越来越重要,但平均而言,配对之间的相似性在过去几十年中并没有改变。这表明,趋同的趋势(例如,多学科性)和发散(例如,学科的专业化是平衡的。

英文摘要:

We use an information-theoretic measure of linguistic similarity to investigate the organization and evolution of scientific fields. An analysis of almost 20M papers from the past three decades reveals that the linguistic similarity is related but different from experts and citation-based classifications, leading to an improved view on the organization of science. A temporal analysis of the similarity of fields shows that some fields (e.g., computer science) are becoming increasingly central, but that on average the similarity between pairs has not changed in the last decades. This suggests that tendencies of convergence (e.g., multi-disciplinarity) and divergence (e.g., specialization) of disciplines are in balance.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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