---
title: "Ancient Greek — papers"
description: "Ancient Greek: 13 papers of the Elamur Research library, the most cited first."
canonical: "https://research.elamur.ai/free/en/topics/ancient-greek/"
---
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# 🏺 Ancient Greek

13 papers · the most cited first · [all topics](https://md.research.elamur.ai/free/en/topics/)

Homer, Plato and the New Testament: the language of ancient Greece and its texts.

A survey · about 8 minutes to read · every paper named here has its own page in this library

**In this article**

1. [What it is](#a-what-it-is)
2. [History and milestones](#a-history-and-milestones)
3. [Main problems and tasks](#a-main-problems-and-tasks)
4. [Methods and schools](#a-methods-and-schools)
5. [Datasets and resources](#a-datasets-and-resources)
6. [Landmark papers in this library](#a-landmark-papers-in-this-library)
7. [People](#a-people)
8. [Institutions and places](#a-institutions-and-places)
9. [Open questions](#a-open-questions)
10. [Neighbouring topics](#a-neighbouring-topics)

Ancient Greek is the language of Homer, of the Athenian historians, dramatists and philosophers, of the Hellenistic koine, and of the Greek Bible: both the Septuagint, the translation of the Hebrew scriptures made in Egypt from the third century BCE onwards, and the New Testament. For more than two thousand years it has been studied, edited and translated, and in recent decades it has also become an object of computational work: morphological analysers, treebanks, lexical databases, sentence encoders and machine translation systems built for a language that has no native speakers and a closed, if very large, corpus.

The papers collected under this topic come from two traditions that meet in the Greek text. One is biblical and philological scholarship, which reads the Septuagint closely against its Hebrew source to see how ancient translators handled idioms, metaphors and difficult words. The other is language technology for historical languages, which builds annotated corpora and models for Greek, often side by side with Hebrew or Latin. A nineteenth-century debate about rendering Homer in English opens the collection.

## What it is

The topic covers research whose object is Greek of the ancient period: Homeric and Archaic Greek, Classical Attic, the Ionic and Doric literary dialects, and Hellenistic and Roman koine, including the Septuagint and the New Testament. Ancient Greek is a richly inflected language with a pitch accent marked in the written tradition, several dialects, free word order and an extensive system of participles and particles.

It differs from its neighbours in a few clear ways. The [Latin](https://md.research.elamur.ai/free/en/topics/latin/) topic shares the same classical setting and several cross-lingual resources, but Latin work in this library is dominated by treebanks and shared tasks. [Hebrew & Aramaic](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) is the closest neighbour by overlap, because so much Greek research here concerns the Septuagint as a translation of Hebrew. The wider [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) topic brings in the same biblical material from the source-language side. Most of the papers stand on the [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) shelf, with smaller shares on [Machine translation](https://md.research.elamur.ai/free/en/shelves/machine-translation/), [History & humanities](https://md.research.elamur.ai/free/en/shelves/history/) and [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/).

## History and milestones

Translation theory for Greek has a long history in English letters. Matthew Arnold's Oxford lectures of 1860–61 and Francis Newman's reply set out two positions that are still recognisable: a translation judged by scholars who can read the original, and one judged by an educated public that cannot. Septuagint studies later grew into a field of their own, with typologies of literal and free renderings.

Computational work on Greek started with digitised texts and lexica, most prominently the Perseus Digital Library and the Thesaurus Linguae Graecae, followed by morphological analysers and, from the late 2000s, dependency treebanks such as the Ancient Greek Dependency Treebank and PROIEL, both later converted into [Universal Dependencies](https://md.research.elamur.ai/free/en/topics/universal-dependencies/). From around 2020, pretrained encoders for Greek and Latin and multilingual sentence encoders made semantic search and cross-lingual alignment feasible. The collection follows that arc: a Victorian classic, a 1990s essay on the Greek Daniel, then mostly recent papers.

Landmark papers on this topic: the 5 most cited, in time The 5 most cited papers on this topic span 2015–2026; the dot grows with the citations.

**The numbers**

| Paper | Citations |
| --- | --- |
| [2015 — Translation Techniques and Interpretative Phenomena in the Greek Version of the Hebrew Bible: A Study of the Figurative Use of the Noun צֵל 'Shadow'](https://md.research.elamur.ai/free/en/papers/vergari-2015-translation-techniques-interpretative-phenomena-greek/) | 1 |
| [2018 — The Biblical Hebrew idiom ‘lift the face’ in the Septuagint of Job](https://md.research.elamur.ai/free/en/papers/mangum-2018-biblical-hebrew-idiom-lift-face/) | 2 |
| [2022 — Handling Stress in Finite-State Morphological Analyzers for Ancient Greek and Ancient Hebrew](https://md.research.elamur.ai/free/en/papers/swanson-2022-handling-stress-finite-state-morphological/) | 2 |
| [2024 — Producing a Parallel Universal Dependencies Treebank of Ancient Hebrew and Ancient Greek via Cross-Lingual Projection](https://md.research.elamur.ai/free/en/papers/swanson-2024-producing-parallel-universal-dependencies-treebank/) | 2 |
| [2026 — Gretino: A Greek and Latin Dataset to Benchmark Retrieval Systems in Classical Languages](https://md.research.elamur.ai/free/en/papers/toyin-2026-gretino-greek-latin-dataset-benchmark/) | 1 |

Papers on this topic by year 13 dated papers from 1861 to 2026 (1 before 1966 are not drawn); the busiest year is 2026 with 5, and 77% of them appeared in 2018 or later.

**The numbers**

| Year | Papers |
| --- | --- |
| 2026 | 5 |
| 2024 | 1 |
| 2022 | 2 |
| 2020 | 1 |
| 2018 | 1 |
| 2015 | 1 |
| 1994 | 1 |
| 1861 | 1 |

## Main problems and tasks

The papers here deal with a recognisable set of problems:

- **Translation technique.** How did the Septuagint translators render Hebrew idioms, figurative nouns and rare verbs? Was the Greek a literal copy or an interpretation? This is the central question for the philological studies.
- **Morphological analysis.** Greek accentuation shifts with inflection, and an analyser must model it if it is to accept and generate correct forms.
- **Syntactic annotation.** Treebanks are expensive to build by hand; projecting annotation from a parallel text in another language is one way to bootstrap them.
- **Lexical semantics.** Linking Greek words to a wordnet requires choosing the right sense for groups of near-synonyms, a task complicated by polysemy.
- **Cross-lingual retrieval and alignment.** Finding Greek–Latin translations and conceptual parallels supports the study of intertextuality and reception.
- **Machine translation into a modern language**, for which sentence-aligned training data has been scarce.

## Methods and schools

Two broad method families coexist. The philological one is close reading: collecting every occurrence of a word or idiom, comparing ancient versions (Greek, Syriac, Aramaic), consulting Greek literature and non-literary papyri for usage, and classifying each rendering. Its evidence is hand-built tables rather than statistics.

The computational one ranges from rule-based to neural. Finite-state transducers, as in Apertium, encode morphology as explicit rules. Annotation projection copies syntactic arcs across word alignments. More recent work uses sentence encoders (LaBSE and Greek- or Latin-specific BERT models), contrastive fine-tuning, whitening and knowledge distillation, and fine-tuned translation models and large language models. Large language models appear both as tools and as judges, with mixed results.

## Datasets and resources

Well-known resources for the language include the Perseus Digital Library, the Thesaurus Linguae Graecae, the Liddell–Scott–Jones lexicon (LSJ) and the Greek treebanks in Universal Dependencies. The papers in this library add several of their own: a parallel UD treebank of Greek Genesis and Ruth aligned to Hebrew, a Greek–Latin semantic retrieval benchmark (Gretino), a Greek–Latin parallel-mining benchmark built around Boethius' Latin Aristotle, and a large sentence-aligned Ancient-to-Modern Greek corpus with a test set and a dialect stress set. These connect the topic to [Treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/) and to [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/).

## Landmark papers in this library

**Projection of a parallel treebank.** [Producing a Parallel Universal Dependencies Treebank of Ancient Hebrew and Ancient Greek via Cross-Lingual Projection](https://md.research.elamur.ai/free/en/papers/swanson-2024-producing-parallel-universal-dependencies-treebank/) (2024) builds Greek trees for Genesis and Ruth by aligning the Septuagint with an existing Hebrew treebank and copying arcs whose endpoints align, then correcting systematic mismatches with rewrite rules and manual checking. Heads transfer better than relation labels.

**Accent in a finite-state analyser.** [Handling Stress in Finite-State Morphological Analyzers for Ancient Greek and Ancient Hebrew](https://md.research.elamur.ai/free/en/papers/swanson-2022-handling-stress-finite-state-morphological/) (2022) shows that accent placement can be handled in finite-state morphology if the lexicon carries extra control symbols. The Greek prototype covers most tokens of the New Testament, while overgenerating a share of forms.

**An idiom across versions.** [The Biblical Hebrew idiom 'lift the face' in the Septuagint of Job](https://md.research.elamur.ai/free/en/papers/mangum-2018-biblical-hebrew-idiom-lift-face/) (2018) classifies how the Greek, Syriac and Aramaic versions render one Hebrew idiom, adding a category for substitution by a target-language idiom. Greek Job turns out to prefer natural Greek phrasing and uses it to shift theological emphasis.

**Semantic search benchmark.** [Gretino: A Greek and Latin Dataset to Benchmark Retrieval Systems in Classical Languages](https://md.research.elamur.ai/free/en/papers/toyin-2026-gretino-greek-latin-dataset-benchmark/) (2026) provides synthetic and authentic query sets for retrieval in either language and across them; contrastive fine-tuning on aligned Greek–Latin text lifts cross-lingual retrieval while leaving single-language scores about where they were.

**A figurative noun in translation.** [Translation Techniques and Interpretative Phenomena in the Greek Version of the Hebrew Bible: A Study of the Figurative Use of the Noun צֵל 'Shadow'](https://md.research.elamur.ai/free/en/papers/vergari-2015-translation-techniques-interpretative-phenomena-greek/) (2015) traces the Hebrew word for "shadow" into Greek and finds that where it means protection, the translators avoided the usual Greek word and chose one meaning "cover", a choice supported by evidence from literature and papyri.

**Ancient to Modern Greek.** [Ancient Greek to Modern Greek Machine Translation: A Novel Benchmark and Fine-Tuning Experiments on LLMs and NMT Models](https://md.research.elamur.ai/free/en/papers/mavromatis-2026-ancient-greek-modern-greek-machine/) (2026) assembles a sentence-aligned corpus from literary, historical and biblical sources and shows that fine-tuning, full or parameter-efficient, improves every NMT system and LLM tested.

**Greek–Latin parallel mining.** [Evaluating Latin and Ancient Greek Sentence Alignment through Parallel Sentence Mining](https://md.research.elamur.ai/free/en/papers/reichbauer-2026-evaluating-latin-ancient-greek-sentence/) (2026) hides known translation pairs among unrelated sentences and measures how well encoders find them; distillation through English plus whitening works best.

**A Greek wordnet step.** [Evaluating Hierarchical Aggregation and LLM-Based Matching for Synset Selection in Ancient Greek](https://md.research.elamur.ai/free/en/papers/villa-2026-evaluating-hierarchical-aggregation-llm-based/) (2026) matches pooled LSJ definitions to English wordnet glosses and finds that a hierarchy-based choice is a sound baseline, while language-model reranking is unreliable for polysemous groups.

**The Greek Daniel as literature.** [A Literary Critical Comparison of the Masoretic Text and Septuagint of Daniel 2-7](https://md.research.elamur.ai/free/en/papers/meadowcroft-1994-literary-critical-comparison-masoretic-text/) (1994) reads the Greek Daniel as a narrative in its own right and contrasts an overt Greek narrator with a covert Hebrew–Aramaic one.

**Smaller studies.** [Do Bashal and Hepsō really mean 'boil'? A preliminary study in the semantics of biblical Hebrew and Septuagint Greek](https://md.research.elamur.ai/free/en/papers/mangum-2022-bashal-hepso-really-mean-boil/) (2022) weighs "boil" against a generic "cook" for a Hebrew verb and its Greek equivalent; [The use of the Septuagint in Afrikaans Bible translations](https://md.research.elamur.ai/free/en/papers/rooy-2020-use-septuagint-afrikaans-bible-translations/) (2020) shows modern translators quietly following the Greek where the Hebrew is difficult; and a [review of Alma Brodersen's study of Psalms 146–150](https://md.research.elamur.ai/free/en/papers/watson-2026-alma-brodersen-end-psalter-psalms/) describes a three-witness method that includes the Septuagint.

**How Homer should sound.** [On Translating Homer; with F. W. Newman's "Homeric Translation in Theory and Practice" and Arnold's "Last Words"](https://md.research.elamur.ai/free/en/papers/arnold-1861-translating-homer-f-w/) (1861) collects Arnold's lectures and Newman's reply, the classic English debate on rapidity, plainness and nobility in rendering Greek epic.

Citations of the papers on this topic, by when they were published Papers published in 2020–2024 gather 50% of the 8 citations of the papers on this topic.

**The numbers**

| Published | Papers and their citations |
| --- | --- |
| 2025–2029 | 5 papers, 1 citations |
| 2020–2024 | 4 papers, 4 citations |
| 2015–2019 | 2 papers, 3 citations |
| 1990–1994 | 1 papers, 0 citations |
| 1860–1864 | 1 papers, 0 citations |

## People

Authors with the most papers on this topic Daniel G. Swanson leads with 2 papers; the 12 authors shown wrote 15 of the papers on this topic between them, counting each paper once per author.

**The numbers**

| Author | Papers here · their citations |
| --- | --- |
| [Daniel G. Swanson](https://md.research.elamur.ai/free/en/authors/daniel-g-swanson/) | 2 papers · 4 citations |
| [Francis Tyers](https://md.research.elamur.ai/free/en/authors/francis-tyers/) | 2 papers · 4 citations |
| [Douglas Mangum](https://md.research.elamur.ai/free/en/authors/douglas-mangum/) | 2 papers · 2 citations |
| [Bryce D. Bussert](https://md.research.elamur.ai/free/en/authors/bryce-d-bussert/) | 1 paper · 2 citations |
| [Hawau Olamide Toyin](https://md.research.elamur.ai/free/en/authors/hawau-olamide-toyin/) | 1 paper · 1 citation |
| [Romina Vergari](https://md.research.elamur.ai/free/en/authors/romina-vergari/) | 1 paper · 1 citation |
| [Alexander Fraser](https://md.research.elamur.ai/free/en/authors/alexander-fraser/) | 1 paper · 0 citations |
| [Luca Brigada Villa](https://md.research.elamur.ai/free/en/authors/luca-brigada-villa/) | 1 paper · 0 citations |
| [Marco Passarotti](https://md.research.elamur.ai/free/en/authors/marco-passarotti/) | 1 paper · 0 citations |
| [Maria Giagkou](https://md.research.elamur.ai/free/en/authors/maria-giagkou/) | 1 paper · 0 citations |
| [Shu Okabe](https://md.research.elamur.ai/free/en/authors/shu-okabe/) | 1 paper · 0 citations |
| [Wilfred G.E. Watson](https://md.research.elamur.ai/free/en/authors/wilfred-g-e-watson/) | 1 paper · 0 citations |

- [Daniel G. Swanson](https://md.research.elamur.ai/free/en/authors/daniel-g-swanson/), now at Indiana University Bloomington, is the most prolific author on this topic, with work on Greek morphology and on the projected Greek–Hebrew treebank.
- [Francis Tyers](https://md.research.elamur.ai/free/en/authors/francis-tyers/), a long-standing contributor to Apertium and Universal Dependencies, co-authored both of those papers ([Bryce D. Bussert](https://md.research.elamur.ai/free/en/authors/bryce-d-bussert/) joined them on the treebank); his listed affiliations include Indiana University and the Center for Applied Linguistics.
- [Douglas Mangum](https://md.research.elamur.ai/free/en/authors/douglas-mangum/), at the University of the Free State, writes on Septuagint lexicography and idiom, from "lift the face" to the verbs for boiling and cooking.
- [Romina Vergari](https://md.research.elamur.ai/free/en/authors/romina-vergari/) studies translation technique through the figurative senses of single Hebrew nouns.
- [Hawau Olamide Toyin](https://md.research.elamur.ai/free/en/authors/hawau-olamide-toyin/), now working in Abu Dhabi, is first author of the Gretino retrieval benchmark.
- [Alexander Fraser](https://md.research.elamur.ai/free/en/authors/alexander-fraser/) and [Shu Okabe](https://md.research.elamur.ai/free/en/authors/shu-okabe/), with the Technical University of Munich, are among the authors of the Greek–Latin parallel-mining study.
- [Luca Brigada Villa](https://md.research.elamur.ai/free/en/authors/luca-brigada-villa/) of the University of Pavia and [Marco Passarotti](https://md.research.elamur.ai/free/en/authors/marco-passarotti/), a central figure in Latin language resources, wrote the synset-selection study.
- [Maria Giagkou](https://md.research.elamur.ai/free/en/authors/maria-giagkou/) co-authored the Ancient-to-Modern Greek translation benchmark.
- [Wilfred G.E. Watson](https://md.research.elamur.ai/free/en/authors/wilfred-g-e-watson/), a scholar of ancient Semitic poetry, reviewed a monograph comparing the last psalms in the Hebrew, Qumran and Greek witnesses.

## Institutions and places

The authors' current homes are spread thinly. [Indiana University](https://md.research.elamur.ai/free/en/universities/indiana-university/) and [Indiana University Bloomington](https://md.research.elamur.ai/free/en/universities/indiana-university-bloomington/) host the treebank and morphology work, and the [University of the Free State](https://md.research.elamur.ai/free/en/universities/university-of-the-free-state/) in South Africa the Septuagint lexicography. In Europe, the [University of Pavia](https://md.research.elamur.ai/free/en/universities/university-of-pavia/), the [Università Cattolica del Sacro Cuore](https://md.research.elamur.ai/free/en/universities/universita-cattolica-del-sacro-cuore/) and [Ghent University](https://md.research.elamur.ai/free/en/universities/ghent-university/) appear through the wordnet study, and the [Technical University of Munich](https://md.research.elamur.ai/free/en/universities/technical-university-of-munich/) and the [Munich Center for Machine Learning](https://md.research.elamur.ai/free/en/universities/munich-center-for-machine-learning/) through Greek–Latin alignment. [Mohamed bin Zayed University of Artificial Intelligence](https://md.research.elamur.ai/free/en/universities/mohamed-bin-zayed-university-of-artificial-intelligence/) is the current home of the Gretino lead author. Venues split between theological journals and the LREC conference family, including the LT4HALA workshops on historical languages.

Where the authors on this topic work now Center for Applied Linguistics is the current home of authors of 2 papers on this topic; counted are the authors with a page here, by their latest institution.

**The numbers**

| Institution | Papers |
| --- | --- |
| [Center for Applied Linguistics](https://ror.org/020pekv35) | 2 papers |
| [Indiana University](https://md.research.elamur.ai/free/en/universities/indiana-university/) | 2 papers |
| [Indiana University Bloomington](https://md.research.elamur.ai/free/en/universities/indiana-university-bloomington/) | 2 papers |
| [Mozilla Foundation](https://ror.org/01y8r3379) | 2 papers |
| [University of the Free State](https://md.research.elamur.ai/free/en/universities/university-of-the-free-state/) | 2 papers |
| [Ghent University](https://md.research.elamur.ai/free/en/universities/ghent-university/) | 1 paper |
| [Mohamed bin Zayed University of Artificial Intelligence](https://md.research.elamur.ai/free/en/universities/mohamed-bin-zayed-university-of-artificial-intelligence/) | 1 paper |
| [Munich Center for Machine Learning](https://md.research.elamur.ai/free/en/universities/munich-center-for-machine-learning/) | 1 paper |
| [Technical University of Munich](https://md.research.elamur.ai/free/en/universities/technical-university-of-munich/) | 1 paper |
| [University of Ha'il](https://ror.org/013w98a82) | 1 paper |

Countries of the authors on this topic South Africa comes first: authors working there now wrote 2 papers on this topic.

**The numbers**

| Country | Papers |
| --- | --- |
| South Africa | 2 papers |
| United States | 2 papers |
| Belgium | 1 paper |
| Germany | 1 paper |
| Italy | 1 paper |
| Saudi Arabia | 1 paper |
| United Arab Emirates | 1 paper |

Where the papers on this topic were published Proceedings of the Fifteenth Language Resources and Evaluation Conference carries the most, 2 of 13.

**The numbers**

| Venue | Papers |
| --- | --- |
| Proceedings of the Fifteenth Language Resources and Evaluation Conference | 2 papers |
| Folia Orientalia 55 | 1 paper |
| HTS Teologiese Studies / Theological Studies | 1 paper |
| HTS Teologiese Studies/Theological Studies 74 | 1 paper |
| HTS Teologiese Studies/Theological Studies 76 | 1 paper |
| Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) | 1 paper |
| Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities | 1 paper |
| Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026 | 1 paper |
| Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages | 1 paper |
| Quaderni di Linguistica e Studi Orientali 1 | 1 paper |

## Open questions

Several questions recur. How far can annotation be projected from Hebrew or Latin before the differences between languages make the copied structure misleading? Can evaluation data for retrieval and alignment be made realistic without contaminating it with texts the models already know? How much of the dialect variety of Greek, from Homeric to Doric, do models trained mostly on Attic and koine really handle? And can a philologist's distinctions, such as idiom substitution versus copying, be encoded in resources scholars can check?

## Neighbouring topics

The strongest link is to [Hebrew & Aramaic](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) and [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/), through the Septuagint. [Latin](https://md.research.elamur.ai/free/en/topics/latin/) shares the cross-lingual retrieval and alignment work. Technical neighbours include [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/), [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/), [Morphology](https://md.research.elamur.ai/free/en/topics/morphology/), [Treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/), [Universal Dependencies](https://md.research.elamur.ai/free/en/topics/universal-dependencies/), [Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/), [Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) and, for the idiom studies, [Idioms & multiword expressions](https://md.research.elamur.ai/free/en/topics/multiword-expressions/).

Neighbouring topics: papers shared Hebrew & Aramaic shares the most: 8 of the 13 papers here (62%) stand on both.

**The numbers**

| Topic | Papers shared |
| --- | --- |
| [Hebrew & Aramaic](https://md.research.elamur.ai/free/en/topics/hebrew-aramaic/) | 8 shared · 62% |
| [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) | 6 shared · 46% |
| [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) | 4 shared · 31% |
| [Latin](https://md.research.elamur.ai/free/en/topics/latin/) | 2 shared · 15% |
| [Idioms & multiword expressions](https://md.research.elamur.ai/free/en/topics/multiword-expressions/) | 1 shared · 8% |
| [Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) | 1 shared · 8% |
| [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) | 1 shared · 8% |
| [Morphology](https://md.research.elamur.ai/free/en/topics/morphology/) | 1 shared · 8% |
| [Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) | 1 shared · 8% |
| [Treebanks](https://md.research.elamur.ai/free/en/topics/treebanks/) | 1 shared · 8% |

The shelves this topic stands on Linguistics & languages: 12 of the 13 papers here.

**The numbers**

| Shelf | Papers |
| --- | --- |
| [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) | 12 papers |
| [Machine translation](https://md.research.elamur.ai/free/en/shelves/machine-translation/) | 4 papers |
| [History & humanities](https://md.research.elamur.ai/free/en/shelves/history/) | 3 papers |
| [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) | 3 papers |

## All 13 papers on this topic

1. [**Producing a Parallel Universal Dependencies Treebank of Ancient Hebrew and Ancient Greek via Cross-Lingual Projection**Daniel G. Swanson, Bryce D. Bussert, Francis Tyers*2024 · 2 citations*](https://md.research.elamur.ai/free/en/papers/swanson-2024-producing-parallel-universal-dependencies-treebank/)
2. [**Handling Stress in Finite-State Morphological Analyzers for Ancient Greek and Ancient Hebrew**Daniel G. Swanson, Francis M. Tyers*2022 · 2 citations*](https://md.research.elamur.ai/free/en/papers/swanson-2022-handling-stress-finite-state-morphological/)
3. [**The Biblical Hebrew idiom ‘lift the face’ in the Septuagint of Job**Douglas Mangum*2018 · 2 citations*](https://md.research.elamur.ai/free/en/papers/mangum-2018-biblical-hebrew-idiom-lift-face/)
4. [**Gretino: A Greek and Latin Dataset to Benchmark Retrieval Systems in Classical Languages**Hawau Olamide Toyin, Federico Iezzi, Elia Scapini, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/toyin-2026-gretino-greek-latin-dataset-benchmark/)
5. [**Translation Techniques and Interpretative Phenomena in the Greek Version of the Hebrew Bible: A Study of the Figurative Use of the Noun צֵל 'Shadow'**Romina Vergari*2015 · 1 citation*](https://md.research.elamur.ai/free/en/papers/vergari-2015-translation-techniques-interpretative-phenomena-greek/)
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12. [**A Literary Critical Comparison of the Masoretic Text and Septuagint of Daniel 2-7**Tim Meadowcroft*1994*](https://md.research.elamur.ai/free/en/papers/meadowcroft-1994-literary-critical-comparison-masoretic-text/)
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