Journal article Open Access
Yanniek van der Schans; David Ruhe; Wido van Peursen; Sandjai Bhulai
{ "description": "<p>This study examines linguistic variation within Biblical Hebrew<br>\nby using Recurrent Neural Networks (RNNs) to detect differences<br>\nand cluster the Old Testament books accordingly. Various linguistic<br>\nfeatures are analysed that are traditionally considered to be of importance in analysing linguistic variation. The traditional division<br>\nof books as either Early Biblical Hebrew or Late Biblical Hebrew is<br>\nhereby put to the test. Results show that RNNs are a fitting method<br>\nfor analysing the (morpho)syntax of a language. The model works<br>\nwell on both separate features, as well as all the features combined.<br>\nOn the basis of the results the RNNs provide, we propose that<br>\nthe diachronic approach to Biblical Hebrew is indeed plausible.<br>\nThe clusters generally hint to the scholarly division made in the<br>\ndiachronic approach to linguistic variation<br>\n </p>", "license": "https://creativecommons.org/licenses/by/4.0/legalcode", "creator": [ { "affiliation": "Vrije Universiteit Amsterdam", "@type": "Person", "name": "Yanniek van der Schans" }, { "affiliation": "Vrije Universiteit Amsterdam", "@type": "Person", "name": "David Ruhe" }, { "affiliation": "Vrije Universiteit Amsterdam", "@type": "Person", "name": "Wido van Peursen" }, { "affiliation": "Vrije Universiteit Amsterdam", "@type": "Person", "name": "Sandjai Bhulai" } ], "headline": "Clustering Biblical Texts Using Recurrent Neural Networks", "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", "datePublished": "2020-08-27", "url": "https://zenodo.org/record/4003509", "keywords": [ "Recurrent Neural Networks", "Biblical Hebrew", "Diachronic Liguistics", "Computational Semantics" ], "@context": "https://schema.org/", "identifier": "https://doi.org/10.5281/zenodo.4003509", "@id": "https://doi.org/10.5281/zenodo.4003509", "@type": "ScholarlyArticle", "name": "Clustering Biblical Texts Using Recurrent Neural Networks" }
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