Hierarchical clustering of a Finnish newspaper article collection with graded relevance assessments |
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Authors: | Tuomo Korenius Jorma Laurikkala Martti Juhola Kalervo Järvelin |
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Institution: | (1) Department of Computer Sciences, University of Tampere, FIN-33014 University of Tampere, Finland;(2) Center for Advanced Studies, University of Tampere, FIN-33014 University of Tampere, Finland |
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Abstract: | Search facilitated with agglomerative hierarchical clustering methods was studied in a collection of Finnish newspaper articles
(N = 53,893). To allow quick experiments, clustering was applied to a sample (N = 5,000) that was reduced with principal components analysis. The dendrograms were heuristically cut to find an optimal partition,
whose clusters were compared with each of the 30 queries to retrieve the best-matching cluster. The four-level relevance assessment
was collapsed into a binary one by (A) considering all the relevant and (B) only the highly relevant documents relevant, respectively.
Single linkage (SL) was the worst method. It created many tiny clusters, and, consequently, searches enabled with it had high
precision and low recall. The complete linkage (CL), average linkage (AL), and Ward's methods (WM) returned reasonably-sized
clusters typically of 18–32 documents. Their recall (A: 27–52%, B: 50–82%) and precision (A: 83–90%, B: 18–21%) was higher
than and comparable to those of the SL clusters, respectively. The AL and WM clustering had 1–8% better effectiveness than
nearest neighbor searching (NN), and SL and CL were 1–9% less efficient that NN. However, the differences were statistically
insignificant. When evaluated with the liberal assessment A, the results suggest that the AL and WM clustering offer better
retrieval ability than NN. Assessment B renders the AL and WM clustering better than NN, when recall is considered more important
than precision. The results imply that collections in the highly inflectional and agglutinative languages, such as Finnish,
may be clustered as the collections in English, provided that documents are appropriately preprocessed. |
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Keywords: | Hierarchical clustering Graded relevance Finnish language Principal components analysis |
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