Historia Augusta authorship: an approach based on Measurements of Complex Networks

Martins, Armando Senra; Grácio, Clara; Teixeira, Cláudia; Rodrigues, Irene Pimenta; Zapata, J.; Ferreira, Lígia

doi:10.1007/s41109-021-00390-7

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…In other words, we needed larger templates with generalized eigenvectors than with usual eigenvectors. An example of our work related to spectral clustering is [6].…”

Section: Introductionmentioning

confidence: 99%

Clustering Vertex-Weighted Graphs by Spectral Methods

Zapata

Grácio

2021

Mathematics

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Spectral techniques are often used to partition the set of vertices of a graph, or to form clusters. They are based on the Laplacian matrix. These techniques allow easily to integrate weights on the edges. In this work, we introduce a p-Laplacian, or a generalized Laplacian matrix with potential, which also allows us to take into account weights on the vertices. These vertex weights are independent of the edge weights. In this way, we can cluster with the importance of vertices, assigning more weight to some vertices than to others, not considering only the number of vertices. We also provide some bounds, similar to those of Chegeer, for the value of the minimal cut cost with weights at the vertices, as a function of the first non-zero eigenvalue of the p-Laplacian (an analog of the Fiedler eigenvalue).

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“…In other words, we needed larger templates with generalized eigenvectors than with usual eigenvectors. An example of our work related to spectral clustering is [6].…”

Section: Introductionmentioning

confidence: 99%

Clustering Vertex-Weighted Graphs by Spectral Methods

Zapata

Grácio

2021

Mathematics

Self Cite

View full text Add to dashboard Cite

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Machine Learning for Ancient Languages: A Survey

Sommerschield,

Assael,

Pavlopoulos

et al. 2023

Computational Linguistics

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Ancient languages preserve the cultures and histories of the past. However, their study is fraught with difficulties, and experts must tackle a range of challenging text-based tasks, from deciphering lost languages to restoring damaged inscriptions, to determining the authorship of works of literature. Technological aids have long supported the study of ancient texts, but in recent years advances in Artificial Intelligence and Machine Learning have enabled analyses on a scale and in a detail that are reshaping the field of Humanities, similarly to how microscopes and telescopes have contributed to the realm of Science. This article aims to provide a comprehensive survey of published research using machine learning for the study of ancient texts written in any language, script and medium, spanning over three and a half millennia of civilisations around the ancient world. To analyse the relevant literature, we introduce a taxonomy of tasks inspired by the steps involved in the study of ancient documents: digitisation, restoration, attribution, linguistic analysis, textual criticism, translation and decipherment. This work offers three major contributions: first, mapping the interdisciplinary field carved out by the synergy between the Humanities and Machine Learning; second, highlighting how active collaboration between specialists from both fields is key to producing impactful and compelling scholarship; third, flagging promising directions for future work in this field. Thus, this work promotes and supports the continued collaborative impetus between the Humanities and Machine Learning.

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Historia Augusta authorship: an approach based on Measurements of Complex Networks

Cited by 2 publications

References 33 publications

Clustering Vertex-Weighted Graphs by Spectral Methods

Clustering Vertex-Weighted Graphs by Spectral Methods

Machine Learning for Ancient Languages: A Survey

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