Abstract
This paper describes and evaluates an unsupervised author clustering model called Spatium. The proposed strategy can be adapted without any difficulty to different natural languages (such as Dutch, English, and Greek) and it can be applied to different text genres (newspaper articles, reviews, excerpts of novels, etc.). As features, we suggest using the m most frequent terms of each text (isolated words and punctuation symbols with m set to at most 200). Applying a distance measure, we define whether there is enough evidence that two texts were written by the same author. The evaluations are based on six test collections (PAN Author Clustering task at CLEF 2016). A more detailed analysis shows the strengths of our approach but also indicates the problems and provides reasons for some of the potential failures of the Spatium model.
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Acknowledgments
The authors want to thank the task coordinators for their valuable effort to promote test collections in authorship attribution. This research was supported, in part, by the NSF under Grant #200021_149665/1.
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Kocher, M., Savoy, J. (2017). Author Clustering with an Adaptive Threshold. In: Jones, G., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2017. Lecture Notes in Computer Science(), vol 10456. Springer, Cham. https://doi.org/10.1007/978-3-319-65813-1_19
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DOI: https://doi.org/10.1007/978-3-319-65813-1_19
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