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Corbara, S.; Moreo, A.; Sebastiani, F.: Syllabic quantity patterns as rhythmic features for Latin authorship attribution (2023)
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- Abstract
- It is well known that, within the Latin production of written text, peculiar metric schemes were followed not only in poetic compositions, but also in many prose works. Such metric patterns were based on so-called syllabic quantity, that is, on the length of the involved syllables, and there is substantial evidence suggesting that certain authors had a preference for certain metric patterns over others. In this research we investigate the possibility to employ syllabic quantity as a base for deriving rhythmic features for the task of computational authorship attribution of Latin prose texts. We test the impact of these features on the authorship attribution task when combined with other topic-agnostic features. Our experiments, carried out on three different datasets using support vector machines (SVMs) show that rhythmic features based on syllabic quantity are beneficial in discriminating among Latin prose authors.
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Debole, F.; Sebastiani, F.: ¬An analysis of the relative hardness of Reuters-21578 subsets (2005)
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Sebastiani, F.: Classification of text, automatic (2006)
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Sebastiani, F.: Machine learning in automated text categorization (2002)
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Sebastiani, F.: ¬A tutorial an automated text categorisation (1999)
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Sebastiani, F.: On the role of logic in information retrieval (1998)
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Giorgetti, D.; Sebastiani, F.: Automating survey coding by multiclass text categorization techniques (2003)
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Fagni, T.; Sebastiani, F.: Selecting negative examples for hierarchical text classification: An experimental comparison (2010)
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