Entropic selection of concepts unveils hidden topics in documents corpora
This incremental work addresses the challenge of uncovering hidden topics in scientific document analysis for researchers in computational linguistics and bibliometrics.
The authors tackled the problem of common concepts obscuring the topical structure in document corpora by introducing an information-theoretic method to identify and remove generic concepts, resulting in a more refined topic organization.
The organization and evolution of science has recently become itself an object of scientific quantitative investigation, thanks to the wealth of information that can be extracted from scientific documents, such as citations between papers and co-authorship between researchers. However, only few studies have focused on the concepts that characterize full documents and that can be extracted and analyzed, revealing the deeper organization of scientific knowledge. Unfortunately, several concepts can be so common across documents that they hinder the emergence of the underlying topical structure of the document corpus, because they give rise to a large amount of spurious and trivial relations among documents. To identify and remove common concepts, we introduce a method to gauge their relevance according to an objective information-theoretic measure related to the statistics of their occurrence across the document corpus. After progressively removing concepts that, according to this metric, can be considered as generic, we find that the topic organization displays a correspondingly more refined structure.