Algorithms for clustering data
Algorithms for clustering data
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Meta-clustering of gene expression data and literature-based information
ACM SIGKDD Explorations Newsletter
Text mining without document context
Information Processing and Management: an International Journal - Special issue: Informetrics
Towards mapping library and information science
Information Processing and Management: an International Journal - Special issue: Informetrics
Text mining techniques for patent analysis
Information Processing and Management: an International Journal
Hybrid clustering for validation and improvement of subject-classification schemes
Information Processing and Management: an International Journal
Generic title labeling for clustered documents
Expert Systems with Applications: An International Journal
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In the present study results of an earlier pilot study by Glenisson, Glänzel and Persson are extended on the basis of larger sets of papers. Full text analysis and traditional bibliometric methods are serially combined to improve the efficiency of the two individual methods. The text mining methodology already introduced in the pilot study is applied to the complete publication year 2003 of the journal Scientometrics. Altogether 85 documents that can be considered research articles or notes have been selected for this exercise. The outcomes confirm the main results of the pilot study, namely, that such hybrid methodology can be applied to both research evaluation and information retrieval. Nevertheless, Scientometrics documents published in 2003 cover a much broader and more heterogeneous spectrum of bibliometrics and related research than those analysed in the pilot study. A modified subject classification based on the scheme used in an earlier study by Schoepflin and Glänzel has been applied for validation purposes.