Lemmatization for Ancient Greek

Vatri, Alessandro; McGillivray, Barbara

doi:10.1163/15699846-02002001

Cited by 8 publications

(8 citation statements)

References 3 publications

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“…For the poetic data, lemmatization accuracy is a little lower than the prose data: accuracy ranges from 0.965 (comedies) to 0.975 (epic poetry) for the poetic data, while most prose genres have an accuracy of more than 0.980 (with oratory and biblical texts on the high end): see Table 4. The lemmas are generally consistent with the LSJ lexicon as well as are not entirely comparable, however: our training set is different than the data that the tools used by Vatri and McGillivray (2020) the lemmas included in the Morpheus codebase (which is largely based on LSJ).…”

Section: Part-of-speech and Morphologymentioning

confidence: 96%

The GLAUx corpus: methodological issues in designing a long-term, diverse, multi-layered corpus of Ancient Greek

Keersmaekers¹

2021

Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021

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This paper describes the GLAUx project ("the Greek Language Automated"), an ongoing effort to develop a large long-term diachronic corpus of Greek, covering sixteen centuries of literary and nonliterary material annotated with NLP methods. After providing an overview of related corpus projects and discussing the general architecture of the corpus, it zooms in on a number of larger methodological issues in the design of historical corpora. These include the encoding of textual variants, handling extralinguistic variation and annotating linguistic ambiguity. Finally, the long-and short-term perspectives of this project are discussed.

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Section: Part-of-speech and Morphologymentioning

confidence: 96%

The GLAUx corpus: methodological issues in designing a long-term, diverse, multi-layered corpus of Ancient Greek

Keersmaekers¹

2021

Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021

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“…Lemmatizers have even been developed for ancient inscriptions such as those written in Ancient Greek (Vatri and McGillivray 2020), Early Irish (i.e. Old and Middle Irish) (Dereza 2018), Classical Armenian, Old Georgian and Syriac (Vidal-Gorène and Kindt 2020), Akkadian (Sahala et al 2023), and additionally for palaeographic 11 th century stone inscriptions as well (Ezhilarasi and Maheswari 2021b).…”

Section: Lemmatizationmentioning

confidence: 99%

A Systematic Review of Computational Approaches to Deciphering Bronze Age Aegean and Cypriot Scripts

Braović,

Krstinić,

Štula

et al. 2024

Computational Linguistics

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This paper provides a detailed insight into computational approaches for deciphering Bronze Age Aegean and Cypriot scripts, namely the Archanes script and the Archanes formula, Phaistos Disk, Cretan hieroglyphic (including the Malia Altar Stone and Arkalochori Axe), Linear A, Linear B, Cypro-Minoan and Cypriot scripts. The unique contributions of this paper are threefold: 1) a thorough review of major Bronze Age Aegean and Cypriot scripts and inscriptions, digital data and corpora associated with them, existing computational decipherment methods developed in order to decipher them, and possible links to other scripts and languages; 2) the definition of 15 major challenges that can be encountered in computational decipherments of ancient scripts; and 3) an outline of a computational model that could possibly be used to simulate traditional decipherment processes of ancient scripts based on palaeography and epigraphy. In the context of this paper the term decipherment denotes the process of discovery of the language and/or the set of symbols behind an unknown script, and the meaning behind it.

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“…Qi et al, 2020). Vatri and McGillivray (2020) compare lemmatizers for Ancient Greek based on dictionary lookup that exploit PoS information to distinguish ambiguous tokens. Alternatively, some approaches do not rely on this type of information at all (e.g.…”

Section: Related Workmentioning

confidence: 99%

Lemmatization Experiments on Two Low-Resourced Languages: Low Saxon and Occitan

Miletić,

Siewert

2023

Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023)

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We present lemmatization experiments on the unstandardized low-resourced languages Low Saxon and Occitan using two machine-learningbased approaches represented by MaChAmp and Stanza. We show different ways to increase training data by leveraging historical corpora, small amounts of gold data and dictionary information, and discuss the usefulness of this additional data. In the results, we find some differences in the performance of the models depending on the language. This variation is likely to be partly due to differences in the corpora we used, such as the amount of internal variation. However, we also observe common tendencies, for instance that sequential models trained only on gold-annotated data often yield the best overall performance and generalize better to unknown tokens.

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Lemmatization for Ancient Greek

Cited by 8 publications

References 3 publications

The GLAUx corpus: methodological issues in designing a long-term, diverse, multi-layered corpus of Ancient Greek

The GLAUx corpus: methodological issues in designing a long-term, diverse, multi-layered corpus of Ancient Greek

A Systematic Review of Computational Approaches to Deciphering Bronze Age Aegean and Cypriot Scripts

Lemmatization Experiments on Two Low-Resourced Languages: Low Saxon and Occitan

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