Mapping subsets of scholarly information

2003/12/11 by Paul Ginsparg, Paul Houle, Thorsten Joachims +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Text Analysis Techniques #Artificial intelligence #Biomedical Text Mining and Ontologies #Computer science #Data science #Field (mathematics) #Information retrieval #Natural language processing #Topic Modeling #World Wide Web #cs.IR #cs.LG

paper · pdf · doi:10.1073/pnas.0308253100

10 pages, 4 figures, presented at Arthur M. Sackler Colloquium on "Mapping Knowledge Domains", 9--11 May 2003, Beckman Center, Irvine, CA, proceedings to appear in PNAS

arxiv created 2003/12/11 · openalex publication_date 2004/04/06 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

We illustrate the use of machine learning techniques to analyze, structure, maintain, and evolve a large online corpus of academic literature. An emerging field of research can be identified as part of an existing corpus, permitting the implementation of a more coherent community structure for its practitioners.

Citations

• Term-weighting approaches in automatic text retrieval
• A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge.
• Visualizing knowledge domains
• A Tutorial on Support Vector Machines for Pattern Recognition

Cited by

• Usage bibliometrics