Author Clustering using Hierarchical Clustering Analysis

This paper presents our approach to the Author Clustering task at PAN 2017. We performed a hierarchical clustering analysis of different document features: typed and untyped character n-grams, and word n-grams. We experimented with two feature representation methods, log-entropy model, and tf-idf; while tuning minimum frequency threshold values to reduce the dimensionality. Our system was ranked 1 in both subtasks, author clustering and authorship-link ranking.

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