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Clustering the Corpus of Seneca: A Lexical-Based Approach

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Book cover Advances in Latent Variables

Part of the book series: Studies in Theoretical and Applied Statistics ((STASSPSS))

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Abstract

We present a lexical-based investigation into the corpus of the opera omnia of Seneca. By applying a number of statistical techniques to textual data we aim to automatically collect similar texts into closely related groups. We demonstrate that our objective and unsupervised method is able to distinguish the texts by work and genre.

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Notes

  1. 1.

    In more detail, the first two principal components explain the 0.902 of the variance, this proportion resulting from the sum of the explaining power of each of the two components (respectively, 0.876 and 0.026).

  2. 2.

    About the attribution of the Hercules Oetaeus see [3, 10, 13]. On the Octavia see [1, 2, 7, 17]. Among the contributions in favour of the authenticity of both the tragedies see [16].

References

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Author information

Authors and Affiliations

  1. Dipartimento di Scienze statistiche, Università Cattolica del Sacro Cuore, Milano, Italy

    Gabriele Cantaluppi

  2. Centro Interdisciplinare di Ricerche per la Computerizzazione dei Segni dell’Espressione, Università Cattolica del Sacro Cuore, Milano, Italy

    Marco Passarotti

Authors
  1. Gabriele Cantaluppi
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  2. Marco Passarotti
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Corresponding author

Correspondence to Gabriele Cantaluppi .

Editor information

Editors and Affiliations

  1. Dept. of Economics and Management, University of Brescia, Brescia, Italy

    Maurizio Carpita

  2. Dept. of Economics and Management, University of Brescia, Brescia, Italy

    Eugenio Brentari

  3. Dept. of Chemometrics and Sensometrics, Oniris Nantes National College, Nantes, France

    El Mostafa Qannari

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Cantaluppi, G., Passarotti, M. (2014). Clustering the Corpus of Seneca: A Lexical-Based Approach. In: Carpita, M., Brentari, E., Qannari, E. (eds) Advances in Latent Variables. Studies in Theoretical and Applied Statistics(). Springer, Cham. https://doi.org/10.1007/10104_2014_6

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