Abstract
We study temporal aspects of authorship attribution - a task which aims to distinguish automatically between texts written by different authors by measuring textual features. This task is important in a number of areas, including plagiarism detection in secondary education, which we study in this work. As the academic abilities of students evolve during their studies, so does their writing style. These changes in writing style form a type of temporal context, which we study for the authorship attribution process by focussing on the students’ more recent writing samples. Experiments with real world data from Danish secondary school students show 84% prediction accuracy when using all available material and 71.9% prediction accuracy when using only the five most recent writing samples from each student.
This type of authorship attribution with only few recent writing samples is significantly faster than conventional approaches using the complete writings of all authors. As such, it can be integrated into working interactive plagiarism detection systems for secondary education, which assist teachers by flagging automatically incoming student work that deviates significantly from the student’s previous work, even during scenarios requiring fast response and heavy data processing, like the period of national examinations.
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Hansen, N.D., Lioma, C., Larsen, B., Alstrup, S. (2014). Temporal Context for Authorship Attribution. In: Lamas, D., Buitelaar, P. (eds) Multidisciplinary Information Retrieval. IRFC 2014. Lecture Notes in Computer Science, vol 8849. Springer, Cham. https://doi.org/10.1007/978-3-319-12979-2_3
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DOI: https://doi.org/10.1007/978-3-319-12979-2_3
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