Abstract
In the last decades speaking and writing habits have changed. Many works faced the author identification task by exploiting frequency-based approaches, numeric techniques or writing style analysis. Following the last approach we propose a technique for author identification based on First-Order Logic. Specifically, we translate the complex data represented by natural language text to complex (relational) patterns that represent the writing style of an author. Then, we model an author as the result of clustering the relational descriptions associated to the sentences. The underlying idea is that such a model can express the typical way in which an author composes the sentences in his writings. So, if we can map such writing habits from the unknown-author model to the known-author model, we can conclude that the author is the same. Preliminary results are promising and the approach seems viable in real contexts since it does not need a training phase and performs well also with short texts.
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Acknowledgments
We wish to express our sincere thanks to Paolo Gissi, for many useful discussions and for the inspiring concept of gray zone. This work was partially funded by Italian FAR project DM19410 MBLab “Laboratorio di Bioinformatica per la Biodiversità Molecolare” and Italian PON 2007-2013 project PON02_00563_3489339 “Puglia@Service”.
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Leuzzi, F., Ferilli, S., Rotella, F. (2014). A Relational Unsupervised Approach to Author Identification. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2013. Lecture Notes in Computer Science(), vol 8399. Springer, Cham. https://doi.org/10.1007/978-3-319-08407-7_14
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DOI: https://doi.org/10.1007/978-3-319-08407-7_14
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