Mixed-membership models of scientific publications
Mixed-membership models of scientific publications
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DOI：
10.1073/pnas.0307760101
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发表时间：
2004-04-06
影响因子：
11.1
通讯作者：
Lafferty, J
中科院分区：
文献类型：
--
作者：
Erosheva, E;Fienberg, S;Lafferty, J
PNAS是世界上被引用次数最多的多学科科学期刊之一。PNAS官方的主题分类结构反映在文章作者提交的主题标签中，主要与传统建立的学科有关。这些包括广泛的领域分类到物理科学，生物科学，社会科学，并进一步细分领域内的分类。以生物科学为重点，我们探讨了一个内部的软分类结构的文章的基础上，只有语义分解的摘要和书目，并比较它与正式的学科分类。我们的模型假设有固定数量的内部类别，每个类别的特征是在单词（摘要）和参考文献（书目）上的多项式分布。每个文章的软分类是基于来自每个类别的文章内容的比例。我们讨论了适当的PNAS数据库的模型，以及其他功能的数据相关的软分类。
PNAS is one of world's most cited multidisciplinary scientific journals. The PNAS official classification structure of subjects is reflected in topic labels submitted by the authors of articles, largely related to traditionally established disciplines. These include broad field classifications into physical sciences, biological sciences, social sciences, and further subtopic classifications within the fields. Focusing on biological sciences, we explore an internal soft-classification structure of articles based only on semantic decompositions of abstracts and bibliographies and compare it with the formal discipline classifications. Our model assumes that there is a fixed number of internal categories, each characterized by multinomial distributions over words (in abstracts) and references (in bibliographies). Soft classification for each article is based on proportions of the article's content coming from each category. We discuss the appropriateness of the model for the PNAS database as well as other features of the data relevant to soft classification.