Author Clustering using Hierarchical Clustering Analysis

This software was developed for the PAN 2017 author clustering competition.

You can find the paper describing our approach at http://ceur-ws.org/Vol-1866/paper_108.pdf.

The author clustering training and test corpus are provided here, but if you use them, cite the corresponding paper: http://ceur-ws.org/Vol-1866/invited_paper_3.pdf

Python 2.7 Requirements

• Numpy
• Scikit-learn
• Gensim
• Scipy
• Bcubed

Using the software

python clusterAuthors.py -i path/to/training/corpus -o path/to/output/directory -c clusteringAlgorithm -w weightingScheme -n featuresThreshold

or

python clusterAuthors.py -h

Please cite

Clustering software: Gómez-Adorno, H., Aleman, Y., Vilariño, D., Sanchez-Perez, M. A., Pinto, D., & Sidorov, G. Author Clustering using Hierarchical Clustering Analysis in CLEF 2017 Working Notes. CEUR Workshop Proceedings, 2017.

Stylometric features: Gomez Adorno, H. M., Rios, G., Posadas Durán, J. P., Sidorov, G., & Sierra, G. (2018). Stylometry-based Approach for Detecting Writing Style Changes in Literary Texts. Computación y Sistemas, 22(1).

Typed n-grams: Markov, Ilia, Efstathios Stamatatos, and Grigori Sidorov. Improving cross-topic authorship attribution: The role of pre-processing. Proceedings of the 18th International Conference on Computational Linguistics and Intelligent Text Processing. CICLing. 2017.