1. J. Kristeva, “Word, Dialog and Novel,” in Toril Moi, ed., The Kristeva Reader, New York: Columbia University Press, 1986, p. 34-61. 2. G. Conte, Latin Literature: A History, Transl. by J. Solodow, rev. by D. Fowler and G. Most, Johns Hopkins University Press, 1994. 3. C. Forstall and W. Scheirer, “Features from Frequency: Authorship and Stylistic Analysis Using Repetitive Sound,” Chicago Colloquium on Digital Humanities and Computer Science, 2009. 4. L. Manevit and M. Yousef, “One-Class SVMs for Document Classification,” Journal of Machine Learning Research (2), pp. 139-154, 2001. 5. M. Platnauer, Latin Elegiac Verse: A Study of the Metrical Usages of Tibullus, Propertius & Ovid. Cambridge University Press, 1951. See for example pp. 36-39. 6. E. Dummler, ed., Poetae Latini Aevi Carolini: Tomus. I. Berlin 1881, pp. 35-86. 7. K. Harrington, J. Pucci, and A.G. Elliott, Medieval Latin (2nd ed.), University of Chicago Press, 1997. 8. N. Coffee, J. Koenig, S. Poornima, and C. Forstall, The Tesserae Project, http:// tesserae.caset.buffalo.edu The study of intertextuality, the shaping of a text’s meaning by other texts, remains a laborious process for the literary critic. Kristeva1 suggests that “Any text is constructed as a mosaic of quotations; any text is the absorption and transformation of another.” The nature of these mosaics is widely varied, from direct quotations representing a simple and overt intertextuality, to more complex transformations that are intentionally or subconsciously absorbed into a text. " Since, in many cases, the problem is one of pattern recognition, it is a good candidate for automated assistance by computers." As a case study for our computational analysis of intertextuality, we turn to Paul the Deacon’s 8th century poem Angustae Vitae, which we suggest has a strong connection to the poetry of Catullus. "
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