Stylometric Classification of Ancient Greek Literary Texts by Genre

Gianitsos, Efthimios; Bolt, Thomas; Chaudhuri, Pramit; Dexter, Joseph P.

doi:10.18653/v1/w19-2507

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…In related work, we have developed a similar feature set for Ancient Greek, which has been used to classify prose and verse and, at a more finegrained level, epic and drama (Gianitsos et al, 2019). Our work on Old English has demonstrated the utility of related features for various literary and attribution studies (Neidorf et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

A Stylometry Toolkit for Latin Literature

Bolt¹,

Flynt²,

Chaudhuri

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Computational stylometry has become an increasingly important aspect of literary criticism, but many humanists lack the technical expertise or language-specific NLP resources required to exploit computational methods. We demonstrate a stylometry toolkit for analysis of Latin literary texts, which is freely available at www.qcrit.org/stylometry. Our toolkit generates data for a diverse range of literary features and has an intuitive pointand-click interface. The features included have proven effective for multiple literary studies and are calculated using custom heuristics without the need for syntactic parsing. As such, the toolkit models one approach to the user-friendly generation of stylometric data, which could be extended to other premodern and non-English languages underserved by standard NLP resources.

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Section: Discussionmentioning

confidence: 99%

A Stylometry Toolkit for Latin Literature

Bolt¹,

Flynt²,

Chaudhuri

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

Self Cite

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“…In recent years, several works have proposed traditional machine learning approaches to the study of ancient texts. This body of work has focused on optical character recognition and visual analysis [31][32][33][34] , writer identification [35][36][37] and text analysis [38][39][40][41][42][43][44] , stylometrics 45 and document dating 46 . It is only very recently that scholarship has begun to use deep learning and neural networks for optical character recognition [47][48][49][50][51][52][53][54][55] , text analysis 56 , machine translation of ancient texts [57][58][59] , authorship attribution 60,61 and deciphering ancient languages 62,63 , and been applied to study the form and style of epigraphic monuments 64 .…”

Section: Previous Workmentioning

confidence: 99%

Restoring and attributing ancient texts using deep neural networks

Assael

Sommerschield

Shillingford

et al. 2022

Nature

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Ancient history relies on disciplines such as epigraphy—the study of inscribed texts known as inscriptions—for evidence of the thought, language, society and history of past civilizations1. However, over the centuries, many inscriptions have been damaged to the point of illegibility, transported far from their original location and their date of writing is steeped in uncertainty. Here we present Ithaca, a deep neural network for the textual restoration, geographical attribution and chronological attribution of ancient Greek inscriptions. Ithaca is designed to assist and expand the historian’s workflow. The architecture of Ithaca focuses on collaboration, decision support and interpretability. While Ithaca alone achieves 62% accuracy when restoring damaged texts, the use of Ithaca by historians improved their accuracy from 25% to 72%, confirming the synergistic effect of this research tool. Ithaca can attribute inscriptions to their original location with an accuracy of 71% and can date them to less than 30 years of their ground-truth ranges, redating key texts of Classical Athens and contributing to topical debates in ancient history. This research shows how models such as Ithaca can unlock the cooperative potential between artificial intelligence and historians, transformationally impacting the way that we study and write about one of the most important periods in human history.

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“…There are few studies on text classification of Greek literature. Most of them show the language independence of their approach in the genre identification task [8,9,35], or test several feature engineering approaches [7,13]. None of the existed studies dealt with the language variety problem, while as far as we know, none of the existed studies have experimented with text classification in Greek literature leveraging transformerbased models.…”

Section: Related Workmentioning

confidence: 99%

“…To the best of our knowledge, this is the first work to use BERT for classifying Greek literature in general. Most of the past studies put more emphasis on feature engineering [9,13].…”

Section: Introductionmentioning

confidence: 99%

Semantic Indexing of 19th-Century Greek Literature Using 21st-Century Linguistic Resources

2021

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Manual classification of works of literature with genre/form concepts is a time-consuming task requiring domain expertise. Building automated systems based on language understanding can help humans to achieve this work faster and more consistently. Towards this direction, we present a case study on automatic classification of Greek literature books of the 19th century. The main challenges in this problem are the limited number of literature books and resources of that age and the quality of the source text. We propose an automated classification system based on the Bidirectional Encoder Representations from Transformers (BERT) model trained on books from the 20th and 21st century. We also dealt with BERT’s constraint on the maximum sequence length of the input, leveraging the TextRank algorithm to construct representative sentences or phrases from each book. The results show that BERT trained on recent literature books correctly classifies most of the books of the 19th century despite the disparity between the two collections. Additionally, the TextRank algorithm improves the performance of BERT.

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Stylometric Classification of Ancient Greek Literary Texts by Genre

Cited by 8 publications

References 22 publications

A Stylometry Toolkit for Latin Literature

A Stylometry Toolkit for Latin Literature

Restoring and attributing ancient texts using deep neural networks

Semantic Indexing of 19th-Century Greek Literature Using 21st-Century Linguistic Resources

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