% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").

@misc{burns2023latincy, 
    title={LatinCy: {Synthetic} Trained Pipelines for Latin {NLP}},
    author={Patrick J. Burns},
    year={2023},
    eprint={2305.04365},
    archiveprefix={arXiv},
    primaryclass={cs.CL},
    url={https://arxiv.org/pdf/2305.04365.pdf}
}

@misc{ströbel, 
    title={pstroe/cc100-latin · Datasets at Hugging Face},
    url={https://huggingface.co/datasets/pstroe/cc100-latin},
    abstractnote={We’re on a journey to advance and democratize artificial intelligence through open source and open science.},
    author={Ströbel, Phillip Benjamin},
    urldate={2023-11-19},
    year={2023},
}

@inproceedings{Erdmann_Wrisley_Allen_Brown_Cohen-Bodénès_Elsner_Feng_Joseph_Joyeux-Prunel_de_Marneffe_2019,
    title = "Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital Humanities",
    author = "Erdmann, Alexander  and
      Wrisley, David Joseph  and
      Allen, Benjamin  and
      Brown, Christopher  and
      Cohen-Bod{\'e}n{\`e}s, Sophie  and
      Elsner, Micha  and
      Feng, Yukun  and
      Joseph, Brian  and
      Joyeux-Prunel, B{\'e}atrice  and
      de Marneffe, Marie-Catherine",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1231",
    doi = "10.18653/v1/N19-1231",
    pages = "2223--2234"
}

@inbook{Celano_2019,
url = {https://doi.org/10.1515/9783110599572-016},
title = {The Dependency Treebanks for Ancient Greek and Latin},
booktitle = {Ancient Greek and Latin in the Digital Revolution},
author = {Giuseppe G.A. Celano},
editor = {Monica Berti},
publisher = {De Gruyter Saur},
address = {Berlin, Boston},
pages = {279--298},
doi = {doi:10.1515/9783110599572-016},
isbn = {9783110599572},
year = {2019},
lastchecked = {2023-12-14}
}

@inproceedings{bamman_crane_2006, 
    title={The Design and Use of a Latin Dependency Treebank},
    author={David Bamman and Gregory R. Crane},
    year={2006},
    url={https://api.semanticscholar.org/CorpusID:16351887},
    booktitle={Proceedings of The Third Workshop on Treebanks and Linguistic Theories},
    pages={67--78},
    address={Tübingen}
}

@inproceedings{dag_t._t._haug_and_marius_l._jøhndal, 
    title={Creating a Parallel Treebank of the Old Indo-European Bible Translations},
    booktitle={Proceedings of the Second Workshop on Language Technology for Cultural Heritage Data},
    editors={Sporleder, Caroline and Ribarov, Kiril},
    pages={27--34},
    url={http://www.lrec-conf.org/proceedings/lrec2008/workshops/W22_Proceedings.pdf#page=31},
    author={Dag T. T. Haug and Marius L. Jøhndal},
    year={2008},
    language={en},
}

@inproceedings{passarotti_dell’orletta,
  title={Improvements in Parsing the Index Thomisticus Treebank. Revision, Combination and a Feature Model for Medieval Latin},
  url={http://www.lrec-conf.org/proceedings/lrec2010/pdf/178_Paper.pdf},
  author={Passarotti, Marco and Dell'Orletta, Felice},
  year={2010},
  booktitle={Proceedings of the Seventh International Conference on Language Resources and Evaluation},
  address={Malta},
  pages={1964--1971}
}

@inproceedings{Cecchini_Passarotti_Marongiu_Zeman_2018,
    title = "Challenges in Converting the Index {T}homisticus Treebank into {U}niversal {D}ependencies",
    author = "Cecchini, Flavio M.  and
      Passarotti, Marco  and
      Marongiu, Paola  and
      Zeman, Daniel",
    editor = "de Marneffe, Marie-Catherine  and
      Lynn, Teresa  and
      Schuster, Sebastian",
    booktitle = "Proceedings of the Second Workshop on Universal Dependencies",
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-6004",
    doi = "10.18653/v1/W18-6004",
    pages = "27--36"
}

@inproceedings{cecchini_sprugnoli_moretti_passarotti_2021, 
    author={Cecchini, Flavio M. and Sprugnoli, Rachele and Moretti, Giovanni and Passarotti, Marco},
    title={{UDante}: {First} Steps Towards the Universal Dependencies Treebank of Dante’s Latin Works},
    booktitle={Proceedings of the Seventh Italian Conference on Computational Linguistics},
    year={2020},
    publisher={Accademia University Press},
    address={Torino},
    url={http://books.openedition.org/aaccademia/8653},
    isbn={9791280136336},
    doi={10.4000/books.aaccademia.8653},
    pages={99--105}
}

@article{korkiakangas_2021, 
    author={Korkiakangas, Timo},
    year={2021},
    month={08},
    pages={191-203},
    title={Late Latin Charter Treebank: {Contents} and annotation},
    volume={16},
    journal={Corpora},
    doi={10.3366/cor.2021.0217},
    url={https://www.academia.edu/76636757/Late_Latin_Charter_Treebank_contents_and_annotation}
}

@inproceedings{johnson-etal-2021-classical,
    title = "The {C}lassical {L}anguage {T}oolkit: {A}n {NLP} Framework for Pre-Modern Languages",
    author = "Johnson, Kyle P.  and
      Burns, Patrick J.  and
      Stewart, John  and
      Cook, Todd  and
      Besnier, Cl{\'e}ment  and
      Mattingly, William J. B.",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-demo.3",
    doi = "10.18653/v1/2021.acl-demo.3",
    pages = "20--29"
}

@misc{Burns_2019,
  title={Building a Text Analysis Pipeline for Classical Languages},
  url={https://www.researchgate.net/publication/335036188_Building_a_Text_Analysis_Pipeline_for_Classical_Languages},
  author={Burns, Patrick J.},
  year={2019},
  month=aug
}

@misc{RDRPOSTagger,
  title={A Rule-based Part-of-Speech and Morphological Tagging Toolkit},
  year={2019},
  url={https://rdrpostagger.sourceforge.net/},
  author={{RDRPOSTagger}}
}

@inproceedings{nguyen_nguyen_pham_pham_2014, 
    address={Gothenburg, Sweden},
    title={{RDRPOSTagger}: {A} Ripple Down Rules-based Part-Of-Speech Tagger},
    url={https://aclanthology.org/E14-2005},
    doi={10.3115/v1/E14-2005},
    booktitle={Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics},
    publisher={Association for Computational Linguistics},
    author={Nguyen, Dat Quoc and Nguyen, Dai Quoc and Pham, Dang Duc and Pham, Son Bao},
    year={2014},
    month={apr},
    pages={17--20},
}

@article{Nguyen_Nguyen_Pham_Pham_2016,
  title={A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging},
  volume={29},
  ISSN={18758452, 09217126},
  DOI={10.3233/AIC-150698},
  abstractNote={In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.},
  note={arXiv:1412.4021 [cs]},
  number={3},
  journal={AI Communications},
  author={Nguyen, Dat Quoc and Nguyen, Dai Quoc and Pham, Dang Duc and Pham, Son Bao},
  year={2016},
  month=apr,
  pages={409–422}
}


@misc{rdrpostagger/models, 
    url={https://github.com/datquocnguyen/RDRPOSTagger/tree/master/Models},
    abstractnote={A  fast and accurate POS and morphological tagging toolkit (EACL 2014) - datquocnguyen/RDRPOSTagger},
    journal={GitHub},
    language={en},
    author={RDRPOSTagger},
    year={2014},
}

 @article{altieri_niyaz_ibraheem_denero, 
    title={Improved Word and Symbol Embedding for Part-of-Speech Tagging},
    url={https://denero.org/content/pubs/snl17_altieri_tagging.pdf},
    abstractnote={State-of-the-art neural part-of-speech (POS) taggers trained only on labeled data from the Penn Treebank have comparable performance to a structure perceptron tagger with handengineered features. This paper explores three modeling techniques for a neural POS tagger that address potential learning challenges at the boundaries of the tagger’s discrete and continuous representations of data. First, a model that predicts each tag independently, based on a joint representation of the input sequence, only has access to the model’s hidden continuous representation of the sentence, but not discrete distributions over neighboring tags. We show that also conditioning on a learned multinomial distribution over the discrete tag space for neighboring positions improves performance. Second, using only one embedding vector for each input symbol leads to high variability in ﬁnal tag accuracy, perhaps due to the challenge of jointly optimizing the embeddings for so many symbols. We show that embedding each symbol twice, in combination with dropout on the embedding layers, also improves performance. Finally, the choice of how each word is decomposed into sub-words affects the way in which continuous parameters are allocated to a discrete sequence of symbols. We describe a new data-driven technique for subword segmentation designed to respect morpheme boundaries. However, our experiments indicate that this change does not improve ﬁnal tag accuracy, despite a large increase in intrinsic segmentation quality when compared with human segmentation annotations. Overall, these three approaches highlight the learning challenges that arise when a model embeds discrete symbols into continuous spaces. Combining these techniques reduces test set errors by 3.8%.},
    author={Altieri, Nicholas and Niyaz, Sherdil and Ibraheem, Samee and DeNero, John},
    language={en},
}
@inproceedings{Acs_Kadar_Kornai_2021, 
    title={Subword Pooling Makes a Difference},
    author={{\'A}cs, Judit  and K{\'a}d{\'a}r, {\'A}kos  and Kornai, Andras},
    editor={Merlo, Paola  and Tiedemann, Jorg  and Tsarfaty, Reut},
    booktitle={Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
    month={apr},
    year={2021},
    address={Online},
    publisher={Association for Computational Linguistics},
    url={https://aclanthology.org/2021.eacl-main.194},
    doi={10.18653/v1/2021.eacl-main.194},
    pages={2284--2295}
}

@article{Brill_1995, 
    title={Transformation-Based Error-Driven Learning and Natural Language Processing: {A} Case Study in Part-of-Speech Tagging},
    author={Brill, Eric},
    editor={Hirschberg, Julia},
    journal={Computational Linguistics},
    volume={21},
    number={4},
    year={1995},
    address={Cambridge, MA},
    url={https://aclanthology.org/J95-4004},
    pages={543--565},
}

@article{Richards_2009, 
    author={Richards, Deborah},
    year={2009},
    month={06},
    pages={159-184},
    title={Two decades of Ripple Down Rules research},
    volume={24},
    journal={Knowledge Eng. Review},
    doi={10.1017/S0269888909000241},
    url={https://www.researchgate.net/publication/220254277_Two_decades_of_Ripple_Down_Rules_research}
}

@misc{johnsonetal2014, 
    author={Johnson, Kyle P. and Patrick Burns and John Stewart and Todd Cook},
    title={{CLTK}: {The} Classical Language Toolkit},
    url={https://github.com/cltk/cltk},
    year={2021},
}

@inproceedings{qi2020stanza, 
    author={Qi, Peng and Zhang, Yuhao and Zhang, Yuhui and Bolton, Jason and Manning, Christopher D.},
    booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations},
    title={Stanza: {A} {Python} Natural Language Processing Toolkit for Many Human Languages},
    url={https://nlp.stanford.edu/pubs/qi2020stanza.pdf},
    year={2020},
    pages={101--108}
}

@misc{UDPipe, 
    title={udpipe: Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing with the {'UDPipe'} {'NLP'} Toolkit},
    url={https://cran.r-project.org/web/packages/udpipe/index.html},
    abstractnote={The data preparation part of any Natural Language Processing flow consists of a number of important steps: Tokenization (1), Parts of Speech tagging (2), Lemmatization (3) and Dependency Parsing (4). This package allows you to do out-of-the-box annotation of these 4 steps and also allows you to train your own annotator models directly from R.},
    author={Wijffels,Jan},
    year={2023},
}

@inproceedings{Straka_2018,
    title = "{UDP}ipe 2.0 Prototype at {C}o{NLL} 2018 {UD} Shared Task",
    author = "Straka, Milan",
    editor = "Zeman, Daniel  and
      Haji{\v{c}}, Jan",
    booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/K18-2020",
    doi = "10.18653/v1/K18-2020",
    pages = "197--207"
}

@misc{Nguyen_2023, 
    type={HTML},
    title={datquocnguyen/{RDRPOST}agger},
    url={https://github.com/datquocnguyen/RDRPOSTagger},
    abstractnote={A fast and accurate POS and morphological tagging toolkit (EACL 2014)},
    author={Nguyen, Dat Quoc},
    year={2023},
    month={jul},
}

@misc{treetagger, 
    url={https://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger},
    author={Schmid, Helmut},
    year={1995},
}

@misc{ling2016finding, 
    title={Finding Function in Form: {Compositional} Character Models for Open Vocabulary Word Representation},
    author={Wang Ling and Tiago Luís and Luís Marujo and Ramón Fernandez Astudillo and Silvio Amir and Chris Dyer and Alan W. Black and Isabel Trancoso},
    year={2016},
    eprint={1508.02096},
    archiveprefix={arXiv},
    primaryclass={cs.CL},
}

@inproceedings{Graves_Schmidhuber_2005, 
    address={Montreal, Que., Canada},
    title={Framewise phoneme classification with bidirectional {LSTM} networks},
    volume={4},
    isbn={978-0-7803-9048-5},
    url={http://ieeexplore.ieee.org/document/1556215/},
    doi={10.1109/IJCNN.2005.1556215},
    abstractnote={In this paper, we apply bidirectional training to a Long Short Term Memory (LSTM) network for the ﬁrst time. We also present a modiﬁed, full gradient version of the LSTM learning algorithm. On the TIMIT speech database, we measure the framewise phoneme classiﬁcation ability of bidirectional and unidirectional variants of both LSTM and conventional Recurrent Neural Networks (RNNs). We ﬁnd that the LSTM architecture outperforms conventional RNNs and that bidirectional networks outperform unidirectional ones.},
    booktitle={Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.},
    publisher={IEEE},
    author={Graves, A. and Schmidhuber, J.},
    year={2005},
    pages={2047–2052},
    language={en},
}


 @book{Hochreiter_Schmidhuber_1997, 
    series={Neural Computation 9, 1735–1780 (1997)},
    title={Long Short-Term Memory},
    url={https://deeplearning.cs.cmu.edu/F23/document/readings/LSTM.pdf},
    abstractnote={Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter’s (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient- based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In compar- isons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunk- ing, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.},
    institution={Massachusetts Institute of Technology},
    author={Hochreiter, Sepp and Schmidhuber, Jürgen},
    publisher={Ronald Williams},
    year={1997},
    collection={Neural Computation 9, 1735–1780 (1997)},
}
@misc{wu2016googles, 
    title={Google's Neural Machine Translation System: {Bridging} the Gap between Human and Machine Translation},
    author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes and Jeffrey Dean},
    year={2016},
    eprint={1609.08144},
    archiveprefix={arXiv},
    primaryclass={cs.CL},
}

@misc{BullingerDigital2020, 
    title={Bullinger Digital},
    copyright={Institut für Computerlinguistik und Institut für Schweizerische Reformationsgeschichte der Universität Zürich},
    url={https://www.bullinger-digital.ch},
    urldate={2023-11-19},
    year={2023},
    author={Bullinger Digital},
}

 @inproceedings{Fischer_Scheurer_Schwitter_Volk_2022,
  address={Marseille},
  title={Machine Translation of 16th Century Letters from Latin to German},
  rights={info:eu-repo/semantics/openAccess},
  url={http://www.lrec-conf.org/proceedings/lrec2022/workshops/LT4HALA/pdf/2022.lt4hala2022-1.7.pdf},
  DOI={10.5167/uzh-218848},
  abstractNote={This paper outlines our work in collecting training data for and developing a Latin–German Neural Machine Translation (NMT) system, for translating 16th century letters. While Latin–German is a low-resource language pair in terms of NMT, the domain of 16th century epistolary Latin is even more limited in this regard. Through our efforts in data collection and data generation, we are able to train a NMT model that provides good translations for short to medium sentences, and outperforms GoogleTranslate overall. We focus on the correspondence of the Swiss reformer Heinrich Bullinger, but our parallel corpus and our NMT system will be of use for many other texts of the time.},
  booktitle={Fischer, Lukas; Scheurer, Patricia; Schwitter, Raphael; Volk, Martin  (2022). Machine Translation of 16th Century Letters from Latin to German.  In: Second Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2022), Marseille, 25 Juni 2022. LREC, 43-50.},
  publisher={LREC},
  author={Fischer, Lukas and Scheurer, Patricia and Schwitter, Raphael and Volk, Martin},
  year={2022},
  month=jun,
  pages={43–50},
  language={eng}
}

@article{Eckhoff_Majer_Welo_Haug_2009, 
    title={Breaking down and putting back together: {Analysis} and synthesis of New Testament Greek},
    volume={9},
    issn={1566-5844, 1569-9846},
    doi={10.1163/156658409X12529372103308},
    number={1},
    journal={Journal of Greek Linguistics},
    author={Eckhoff, Hanne and Majer, Marek and Welo, Eirik and Haug, Dag},
    year={2009},
    pages={56--92},
    url={https://www.researchgate.net/publication/233601585_Breaking_down_and_putting_back_together_analysis_and_synthesis_of_New_Testament_Greek}
}

@misc{ITTB, 
    title={Index Thomisticus Treebank | Projet},
    url={https://itreebank.marginalia.it/view/projet.php#},
    author={The Index Thomisticus Treebank Project},
    year={2015},
}

@misc{RNNTagger, 
    url={https://www.cis.uni-muenchen.de/~schmid/tools/RNNTagger/},
    year={2019},
    author={Schmid,Helmut},
}

@inproceedings{Schmid_2019, 
    author={Schmid, Helmut},
    title={Deep Learning-Based Morphological Taggers and Lemmatizers for Annotating Historical Texts},
    year={2019},
    isbn={9781450371940},
    publisher={Association for Computing Machinery},
    url={https://doi.org/10.1145/3322905.3322915},
    doi={10.1145/3322905.3322915},
    booktitle={Proceedings of the 3rd International Conference on Digital Access to Textual Cultural Heritage},
    pages={133–137},
    numpages={5},
    address={Brussels, Belgium},
    series={DATeCH2019},
}

@misc{UD-Latin-LLCT,
  url={https://universaldependencies.org/treebanks/la_llct/index.html},
  author={Universal Dependencies},
  year={2022}
}

@misc{Korkiakangas,
  title={Late Latin Charter Treebank},
  author={Korkiakangas, Timo},
  language={en},
  url={https://lila-erc.eu/wp-content/uploads/2019/06/korkiakangas.pdf},
  year={2019}
}

@misc{UD-Latin-LLCT-GitHub-2022,
  url={https://github.com/UniversalDependencies/UD_Latin-LLCT},
  publisher={Universal Dependencies},
  year={2022},
  month=aug,
  author={Zeman, Dan and Stormur}
}

@misc{UDante-GitHub-2021,
  url={https://github.com/UniversalDependencies/UD_Latin-UDante},
  publisher={Universal Dependencies},
  year={2021},
  month=nov,
  author={Zeman, Dan and Stormur and passarom}
}

@misc{UD-Latin-UDante,
  url={https://universaldependencies.org/treebanks/la_udante/index.html},
  author={Universal Dependencies},
  year={2022}
}

@misc{UD-Latin-Perseus,
  url={https://universaldependencies.org/treebanks/la_perseus/index.html},
  author={Universal Dependencies},
  year={2022}
}

@misc{perseus-2.1, 
    title={treebank\_data/v2.1/Latin at master {PerseusDL/treebank\_data}},
    url={https://github.com/PerseusDL/treebank_data/tree/master/v2.1/Latin},
    abstractnote={Perseus Treebank Data. Contribute to PerseusDL/treebank\_data development by creating an account on GitHub.},
    year={2021},
    journal={GitHub},
    language={en},
    author={PerseusDL},
}

@misc{OpenAI, 
    title={{OpenAI} Platform},
    url={https://platform.openai.com},
    abstractnote={Explore developer resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI’s platform.},
    language={en},
    author={OpenAI},
    urldate={2023-11},
    year={2023},
}

@misc{Desagulier_2018, 
    type={Billet},
    title={{POS-tagging} in R with {UDPipe}},
    url={https://corpling.hypotheses.org/4081},
    abstractnote={POS stands for “part-of-speech” (i.e. the grammatical nature of a word). POS tagging is the process of assigning a part-of-speech (such as noun, verb, adverb, adjective, determiner, etc.) to each word in a given...},
    journal={Around the word},
    author={Desagulier, Guillaume},
    year={2018},
    month={jan},
    language={fr-FR},
}


 @misc{History_of_Latin_2023, rights={Creative Commons Attribution-ShareAlike License}, title={{H}istory of Latin}, url={https://en.wikipedia.org/w/index.php?title=History_of_Latin&oldid=1173498874}, abstractNote={Latin is a member of the broad family of Italic languages. Its alphabet, the Latin alphabet, emerged from the Old Italic alphabets, which in turn were derived from the Etruscan, Greek and Phoenician scripts. Historical Latin came from the prehistoric language of the Latium region, specifically around the River Tiber, where Roman civilization first developed. How and when Latin came to be spoken has long been debated.
Various influences on Latin of Celtic dialects in northern Italy, the non-Indo-European Etruscan language in Central Italy, and the Greek in some Greek colonies of southern Italy have been detected, but when these influences entered the native Latin is not known for certain.
Surviving Roman-era Latin literature consists almost entirely of Classical Latin pieces usually chosen for their importance as help for people learning to write in Latin. Survivals emphasise polished and sometimes highly stylized literary language texts sometimes termed Golden Latin, which spans the 1st century BC and the early years of the 1st century AD.
As with any written language, the spoken language differed somewhat in grammar, tone and vocabulary, and is referred to as Vulgar Latin. However, theories that the spoken and written languages were more or less different, separated by class or elite education, are now generally rejected.In addition to Latin, the Greek language was often spoken by the well-educated elite, who studied it in school and acquired Greek tutors from among the influx of enslaved educated Greek prisoners of war, captured during the Roman conquest of Greece. In the eastern half of the Roman Empire, later referred to as the Byzantine Empire, the Greek Koine of Hellenism remained current among peasants and traders, while Latin was used for laws and administrative writings. It continued to influence the Vulgar Latin that would evolve into the Eastern Romance languages.
Latin had a long working life beyond the Roman period, as it was the language of the Roman Catholic Church, and later of the Carolingian Holy Roman Empire. It was the dominant language of European learning, literature and academia through the middle ages, and in the early modern period. Latin’s relevance as a widely used working language ended around 1800, although examples of its productive use extend well into that century, and in the cases of the Catholic Church and Classical studies, continue to the present day. As a result, the vast majority - over 99.99% of extant Latin texts - belong to these later periods, and especially to the Neo-Latin period.}, journal={Wikipedia}, year={2023}, language={en} ,urldate = {2023-11-19}, author={Wikipedia},
  rights={Creative Commons Attribution-ShareAlike License},}

@book{mcgillivray2013methods,
  title={Methods in Latin computational linguistics},
  author={McGillivray, Barbara},
  volume={1},
  year={2013},
  publisher={Brill},
  address={Boston}
}

@inproceedings{Hedderich_et_al_2021,
    title = "A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios",
    author = {Hedderich, Michael A.  and
      Lange, Lukas  and
      Adel, Heike  and
      Str{\"o}tgen, Jannik  and
      Klakow, Dietrich},
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-main.201",
    doi = "10.18653/v1/2021.naacl-main.201",
    pages = "2545--2568"
}

@misc{wiki_pos,
  title = {Part-of-speech tagging},
  rights = {Creative Commons Attribution-ShareAlike License},
  url = {https://en.wikipedia.org/w/index.php?title=Part-of-speech_tagging&oldid=1175867470},
  abstractNote = {In corpus linguistics, part-of-speech tagging (POS tagging or PoS tagging or POST), also called grammatical tagging is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context.
  A simplified form of this is commonly taught to school-age children, in the identification of words as nouns, verbs, adjectives, adverbs, etc.
  Once performed by hand, POS tagging is now done in the context of computational linguistics, using algorithms which associate discrete terms, as well as hidden parts of speech, by a set of descriptive tags. POS-tagging algorithms fall into two distinctive groups: rule-based and stochastic. E. Brill’s tagger, one of the first and most widely used English POS-taggers, employs rule-based algorithms.},
  journal = {Wikipedia},
  year = {2023},
  month = sep,
  language = {en},
  author = {Wikipedia}
}

@misc{Tutorialspoint, 
    title={Part of Speech (PoS) Tagging},
    url={https://www.tutorialspoint.com/natural_language_processing/natural_language_processing_part_of_speech_tagging.htm},
    abstractnote={Part of Speech (PoS) Tagging - Tagging is a kind of classification that may be defined as the automatic assignment of description to the tokens. Here the descriptor is called tag, which may represent one of the part-of-speech, semantic information and so on.},
    language={en},
    author={Tutorialspoint},
    urldate={2023-11-18},
    year={2023},
}

@misc{Otten_2023, 
    title={Part-of-speech {(POS)} Tagging In {NLP}: {4} Python How To Tutorials},
    url={https://spotintelligence.com/2023/01/24/part-of-speech-pos-tagging-in-nlp-python/},
    abstractnote={What is Part-of-speech (POS) tagging? Part-of-speech (POS) tagging is fundamental in natural language processing (NLP) and can be done in Python. It involves},
    journal={Spot Intelligence},
    author={Otten, Neri Van},
    year={2023},
    month={jan},
    language={en-US},
}

@article{Chiche_Yitagesu_2022, 
    title={Part of speech tagging: {A} systematic review of deep learning and machine learning approaches},
    volume={9},
    issn={2196-1115},
    doi={10.1186/s40537-022-00561-y},
    abstractnote={Natural language processing (NLP) tools have sparked a great deal of interest due to rapid improvements in information and communications technologies. As a result, many different NLP tools are being produced. However, there are many challenges for developing efficient and effective NLP tools that accurately process natural languages. One such tool is part of speech (POS) tagging, which tags a particular sentence or words in a paragraph by looking at the context of the sentence/words inside the paragraph. Despite enormous efforts by researchers, POS tagging still faces challenges in improving accuracy while reducing false-positive rates and in tagging unknown words. Furthermore, the presence of ambiguity when tagging terms with different contextual meanings inside a sentence cannot be overlooked. Recently, Deep learning (DL) and Machine learning (ML)-based POS taggers are being implemented as potential solutions to efficiently identify words in a given sentence across a paragraph. This article first clarifies the concept of part of speech POS tagging. It then provides the broad categorization based on the famous ML and DL techniques employed in designing and implementing part of speech taggers. A comprehensive review of the latest POS tagging articles is provided by discussing the weakness and strengths of the proposed approaches. Then, recent trends and advancements of DL and ML-based part-of-speech-taggers are presented in terms of the proposed approaches deployed and their performance evaluation metrics. Using the limitations of the proposed approaches, we emphasized various research gaps and presented future recommendations for the research in advancing DL and ML-based POS tagging.},
    number={1},
    journal={Journal of Big Data},
    author={Chiche, Alebachew and Yitagesu, Betselot},
    year={2022},
    month={jan},
    pages={10},
    url={https://journalofbigdata.springeropen.com/articles/10.1186/s40537-022-00561-y}
}

@misc{LLM, 
    title={Was ist ein großes Sprachmodell?},
    author={Elastic},
    year={2023},
    url={https://www.elastic.co/de/what-is/large-language-models},
}

@misc{LLM_wiki,
  title = {Large language model},
  rights = {Creative Commons Attribution-ShareAlike License},
  author = {Wikipedia},
  url = {https://en.wikipedia.org/w/index.php?title=Large_language_model&oldid=1183348924},
  abstractNote = {A large language model (LLM) is a type of language model notable for its ability to achieve general-purpose language understanding and generation. LLMs acquire these abilities by using massive amounts of data to learn billions of parameters during training and consuming large computational resources during their training and operation. LLMs are artificial neural networks (mainly transformers) and are (pre-)trained using self-supervised learning and semi-supervised learning.
  As autoregressive language models, they work by taking an input text and repeatedly predicting the next token or word. Up to 2020, fine tuning was the only way a model could be adapted to be able to accomplish specific tasks. Larger sized models, such as GPT-3, however, can be prompt-engineered to achieve similar results. They are thought to acquire embodied knowledge about syntax, semantics and “ontology” inherent in human language corpora, but also inaccuracies and biases present in the corpora. Notable examples include OpenAI’s GPT models (e.g., GPT-3.5 and GPT-4, used in ChatGPT), Google’s PaLM (used in Bard), and Meta’s LLaMa, as well as BLOOM, Ernie 3.0 Titan, and Anthropic’s Claude 2.},
  journal = {Wikipedia},
  year = {2023},
  month = nov,
  language = {en},
  urldate = {2023-11-18}
}

@article{Guarasci_2017, 
    title={Developing an annotator for Latin texts using Wikipedia},
    url={https://hal.science/hal-01279853},
    abstractnote={This work investigates the feasibility of using Wikipedia as a resource for annotations of Latin texts. Although Wikipedia is an excellent resource from which to extract many kinds of information (morphological, syntactic and semantic) to be used in NLP tasks on modern languages, it was rarely applied to perform NLP tasks for the Latin language. The work presents the first steps of the development of a POS Tagger based on the Latin version of Wiktionary and a Wikipedia-based semantic annotator.},
    journal={Journal of Data Mining and Digital Humanities},
    author={Guarasci, Raffaele},
    year={2017},
}

@incollection{Jurafsky_Martin_2019, 
    booktitle={Speech and Language Processing},
    title={Part-of-Speech Tagging},
    url={https://web.stanford.edu/~jurafsky/slp3/old_oct19/8.pdf},
    author={Jurafsky, Daniel and Martin, James H.},
    year={2019},
    month={oct},
    language={English},
}

@inproceedings{Stoeckel_et_al_2020, 
    title={Voting for {POS} tagging of {L}atin texts: {Using} the flair of {FLAIR} to better Ensemble Classifiers by Example of {L}atin},
    author={Stoeckel, Manuel  and Henlein, Alexander  and Hemati, Wahed  and Mehler, Alexander},
    editor={Sprugnoli, Rachele  and Passarotti, Marco},
    booktitle={1st Workshop on Language Technologies for Historical and Ancient Languages, Proceedings},
    month={may},
    year={2020},
    address={Marseille, France},
    publisher={European Language Resources Association},
    url={https://aclanthology.org/2020.lt4hala-1.21},
    pages={130--135},
    language={English},
    isbn={979-10-95546-53-5},
}


@misc{Chu2023, 
    title={{GPT-4} is a very good Hongkongese {POS} Tagger},
    author={Chu, Lai-Sik Fan},
    year={2023},
    month={mar},
    journal={Medium},
    url={https://medium.com/@kyubi_fox/gpt-4-is-a-very-good-hongkongese-pos-tagger-feeae1b44868},
    language={en},
}

@misc{bamman2020latin, 
    title={Latin {BERT}: {A} Contextual Language Model for Classical Philology},
    author={David Bamman and Patrick J. Burns},
    year={2020},
    eprint={2009.10053},
    archiveprefix={arXiv},
    primaryclass={cs.CL},
    url={https://arxiv.org/abs/2009.10053}
}


@article{passarotti_2022, 
    title={{NLP} Tools for Latin and the {LiLa} Knowledge Base. Lemmatization, {PoS} Tagging and Interoperability},
    url={https://zenodo.org/records/7257985},
    doi={10.5281/zenodo.7257985},
    abstractnote={Slides of the presentation by Marco Passarotti on “NLP Tools for Latin and the LiLa Knowledge Base.Lemmatization, PoS Tagging and Interoperability”, 27th October 2022, University of Zurich, Switzerland.},
    language={en},
}

@inproceedings{passarotti_et_al-2017,
    title = "The Lemlat 3.0 Package for Morphological Analysis of {L}atin",
    author = "Passarotti, Marco  and
      Budassi, Marco  and
      Litta, Eleonora  and
      Ruffolo, Paolo",
    editor = "Bouma, Gerlof  and
      Adesam, Yvonne",
    booktitle = "Proceedings of the {N}o{D}a{L}i{D}a 2017 Workshop on Processing Historical Language",
    month = may,
    year = "2017",
    address = "Gothenburg",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://aclanthology.org/W17-0506",
    pages = "24--31",
}
@article{Passarotti_et_al, 
    title={The {LiLa} Knowledge Base of Interoperable Linguistic Resources for Latin. Architecture and Current State},
    url={https://wp.unil.ch/llist/files/2022/06/COMHUM_2022_paper_1.pdf},
    year={2023},
    abstractnote={This abstract presents the architecture and the current state of the LiLa Knowledge Base, i.e. a collection of multifarious linguistic resources for Latin described with the same vocabulary of knowledge description and interlinked according to the principles of the so-called Linked Data paradigm. This abstract presents the architecture of the LiLa Knowledge Base, whose core consists of a large collection of Latin lemmas, serving as the backbone to achieve interoperability between the resources by linking all those entries in lexical resources and tokens in corpora that point to the same lemma. Moreover, the abstract details the linguistic resources for Latin currently interlinked through LiLa and informs about how the Knowledge Base can be queried.},
    author={Passarotti, Marco and Litta, Eleonora and Cecchini, Flavio Massimiliano and Pellegrini, Matteo and Moretti, Giovanni and Ruffolo, Paolo and Pedonese, Giulia},
    language={en},
}

@article{Passarotti_et_al_2020,
  title = {Interlinking through Lemmas. The Lexical Collection of the LiLa Knowledge Base of Linguistic Resources for Latin},
  volume = {58},
  rights = {Copyright (c) 2020 Studi e Saggi Linguistici},
  ISSN = {2281-9142},
  DOI = {10.4454/ssl.v58i1.277},
  abstractNote = {This paper presents the structure of the LiLa Knowledge Base, i.e. a collection of multifarious linguistic resources for Latin described with the same vocabulary of knowledge description and interlinked according to the principles of the so-called Linked Data paradigm. Following its highly lexically based nature, the core of the LiLa Knowledge Base consists of a large collection of Latin lemmas, serving as the backbone to achieve interoperability between the resources, by linking all those entries in lexical resources and tokens in corpora that point to the same lemma. After detailing the architecture supporting LiLa, the paper particularly focuses on how we approach the challenges raised by harmonizing different strategies of lemmatization that can be found in linguistic resources for Latin. As an example of the process to connect a linguistic resource to LiLa, the inclusion in the Knowledge Base of a dependency treebank is described and evaluated.},
  number = {11},
  journal = {Studi e Saggi Linguistici},
  author = {Passarotti, Marco and Mambrini, Francesco and Franzini, Greta and Cecchini, Flavio Massimiliano and Litta, Eleonora and Moretti, Giovanni and Ruffolo, Paolo and Sprugnoli, Rachele},
  year = {2020},
  month = sep,
  pages = {177–212},
  language = {en}
}

@inproceedings{Franzini_et_al, 
    title={Nunc Est Aestimandum: {Towards} an Evaluation of the Latin WordNet.},
    author={Franzini, Greta and Peverelli, Andrea and Ruffolo, Paolo and Passarotti, Marco and Sanna, Helena and Signoroni, Edoardo and Ventura, Viviana and Zampedri, Federica and others},
    booktitle={CLiC-it},
    year={2019},
    url={https://doi.org/10.4454/ssl.v58i1.277},
}
@misc{ChatGPT_2023, 
    url={https://unimatrixz.com/topics/ai-text/essential-chatgpt-tasks-an-overview/},
    abstractnote={Discover the essential NLP tasks that make ChatGPT so powerful. Learn about named entity recognition, text classification, sentiment analysis, and advanced reasoning, which contribute to ChatGPT’s language processing capabilities. Find out how this makes it a versatile tool for various language-related applications.},
    journal={Uni Matrix Zero},
    year={2023},
    month={apr},
    language={en-us},
}

@mastersthesis{brandle2022, 
    title={Machine Translation of Complex Sentences from Latin to German},
    author={Brändle, Sabrina},
    year={2022},
    school={Faculty of Arts and Social Sciences, University Name},
    address={University Name},
    note={Master's Thesis for obtaining the academic degree Master of Arts},
    student_id={14-705-164},
    advisor={Fischer, Lukas},
    supervisor={Volk, Martin},
    department={Department of Computational Linguistics},
    submission_date={01.12.2022},
    url={www.cl.uzh.ch/dam/jcr:8f2e3f71-d6f0-43ff-b738-bc696e31e210/BRAENDLE-Sabrina_MA_MTComplexSentences_HS22.pdf},
}

@inproceedings{church_1988,
    title = "A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Text",
    author = "Church, Kenneth Ward",
    booktitle = "Second Conference on Applied Natural Language Processing",
    month = feb,
    year = "1988",
    address = "Austin, Texas, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/A88-1019",
    doi = "10.3115/974235.974260",
    pages = "136--143",
} 
@article{Kempe_1993,
  title = {A Probabilistic Tagger and an Analysis of Tagging Errors},
  url = {http://a.kempe.free.fr/reports/kempe93a/kempe93a.pdf},
  abstractNote = {The article describes a tagger which is based on a modiﬁed version of the Viterbi algorithm and uses optionally either trigrams or bigrams. The tagger is implemented in C language. It tags about 280 words per second and has an accuracy of 96.0\%. We discuss smoothing methods for low frequencies and other factors which inﬂuence the accuracy and make a detailed error analysis.},
  author = {Kempe, Andre},
  year = {1993},
  language = {en}, journal = {Kempe}
}

@inproceedings{Schmid_1994, 
    address={Manchester},
    title={Probabilistic Part-of-Speech Tagging Using Decision Trees},
    url={https://www.cis.lmu.de/~schmid/tools/TreeTagger/data/tree-tagger1.pdf},
    booktitle={Proceedings of International Conference on New Methods in Language Processing},
    author={Schmid, Helmut},
    year={1994},
}

@misc{brown2020language, 
    title={Language Models are Few-Shot Learners},
    author={Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and Sandhini Agarwal and Ariel Herbert-Voss and Gretchen Krueger and Tom Henighan and Rewon Child and Aditya Ramesh and Daniel M. Ziegler and Jeffrey Wu and Clemens Winter and Christopher Hesse and Mark Chen and Eric Sigler and Mateusz Litwin and Scott Gray and Benjamin Chess and Jack Clark and Christopher Berner and Sam McCandlish and Alec Radford and Ilya Sutskever and Dario Amodei},
    year={2020},
    url={https://arxiv.org/abs/2005.14165},
    eprint={2005.14165},
    archiveprefix={arXiv},
    primaryclass={cs.CL},
}
 @misc{openai2023gpt4, 
    title={{GPT-4} Technical Report},
    author={OpenAI},
    year={2023},
    eprint={2303.08774},
    archiveprefix={arXiv},
    primaryclass={cs.CL},
    url={https://arxiv.org/pdf/2303.08774.pdf},
}

@misc{Koshti_2023, 
    title={Understanding the {GPT-3.5} Architecture!},
    url={https://www.linkedin.com/pulse/chatgpts-guide-understanding-gpt-35-architecture-heena-koshti},
    abstractnote={Artificial intelligence has come a long way in the last few decades. From simple rule-based systems to deep neural networks, the field of AI has seen significant progress.},
    author={Koshti, Heena},
    year={2023},
    month={mar},
    language={de},
}

@misc{GPT_2023, 
    title={{GPT-3.5} model architecture},
    url={https://iq.opengenus.org/gpt-3-5-model/},
    abstractnote={GPT-3.5 model is a fined-tuned version of the GPT3 (Generative Pre-Trained Transformer) model. GPT-3.5 was developed in January 2022 and has 3 variants each with 1.3B, 6B and 175B parameters. The main feature of GPT-3.5 was to eliminate toxic output to a certain extent.},
    journal={OpenGenus IQ: Computing Expertise \& Legacy},
    year={2023},
    month={jan},
    language={en},
    author={OpenGenus IQ},
}

@misc{Gupta_2023, 
    title={Chat {GPT} and {GPT} 3 Detailed Architecture Study-Deep {NLP} Horse},
    url={https://medium.com/nerd-for-tech/gpt3-and-chat-gpt-detailed-architecture-study-deep-nlp-horse-db3af9de8a5d},
    abstractnote={A detailed intuition and methodology behind the GPT and Chat GPT Language Models.},
    journal={Nerd For Tech},
    author={Gupta, Yashu},
    year={2023},
    month={mar},
    language={en},
}

@misc{Johnson_1015, 
    title={Updated accuracies for {POS} taggers},
    url={http://cltk.org/blog/2015/08/02/updated-accuracies-pos-taggers.html},
    author={Johnson, Kyle P.},
    year={2015},
}

@misc{POS_Tagging, 
    url={https://botpenguin.com/glossary/parts-of-speech-tagging},
    abstractnote={Explore the importance of Parts-of-Speech (POS) tagging in natural language processing. Learn how POS tagging algorithms enable advanced language analysis.},
    language={en},
    author={BotPenguin},
    urldate={2023-11-18},
    year={2023},
}

@book{Sprungoli_Passarotti_2020, 
    title={1st Workshop on Language Technologies for Historical and Ancient Languages, Proceedings},
    isbn={979-10-95546-53-5},
    url={https://lrec2020.lrec-conf.org/media/proceedings/Workshops/Books/LT4HALAbook.pdf},
    author={Sprungoli, Rachele and Passarotti, Marco},
    year={2020},
    month={may},
    publisher={European Language Resources Association},
    address={Paris, France},
    organization={European Language Resources Association},
}

@misc{straka2020UDPipe,
    title={{UDPipe} at EvaLatin 2020: {Contextualized} Embeddings and Treebank Embeddings},
    author={Milan Straka and Jana Straková},
    year={2020},
    eprint={2006.03687},
    archiveprefix={arXiv},
    primaryclass={cs.CL},
    url={https://arxiv.org/pdf/2006.03687.pdf}
}

 @inproceedings{Fantoli_et_al_2022,
    author={Fantoli, Marco and Passarotti, Marco and Mambrini, Francesco and Moretti, Giovanni and Ruffolo, Paolo},
    title={Linking the {LASLA} Corpus in the LiLa Knowledge Base of Interoperable Linguistic Resources for Latin},
    booktitle={Proceedings of the 8th Workshop on Linked Data in Linguistics Within the 13th Language Resources and Evaluation Conference},
    pages={26--34},
    year={2022},
    doi={10.5281/zenodo.6659314},
    url={https://doi.org/10.5281/zenodo.6659314},
}

@inproceedings{Pellegrini_et_al,
  title={{LiLa} Linking Latin Tutorial},
  author={Pellegrini, Matteo and Litta, Eleonora and Passarotti, Marco and Sprugnoli, Rachele and Mambrini, Francesco and Moretti, Giovanni},
  booktitle={Proceedings of the Workshops and Tutorials-Language Data and Knowledge},
  pages={229--234},
  year={2021},
  address={Spain},
  url={https://ceur-ws.org/Vol-3064/LDK2021_Tutorial_Paper.pdf}
}

@inproceedings{Nehrdich_Hellwig_2022, 
    title={Accurate Dependency Parsing and Tagging of {L}atin},
    author={Nehrdich, Sebastian  and Hellwig, Oliver},
    editor={Sprugnoli, Rachele  and Passarotti, Marco},
    booktitle={Proceedings of the Second Workshop on Language Technologies for Historical and Ancient Languages},
    month={jun},
    year={2022},
    address={Marseille, France},
    publisher={European Language Resources Association},
    url={https://aclanthology.org/2022.lt4hala-1.3},
    pages={20--25},
}

@inproceedings{Mcgillivray_and_Passarotti_2009,
  author = {McGillivray, Barbara and Passarotti, Marco},
  title = {The Development of the Index Thomisticus Treebank Valency Lexicon},
  year = {2009},
  month = {January},
  doi = {10.3115/1642049.1642055},
  booktitle = {Workshop on Language Technology and Resources for Cultural Heritage, Social Sciences, Humanities, and Education},
  url = {https://aclanthology.org/W09-0306},
}

@incollection{dinkova-bruun2011, 
    author={Dinkova-Bruun, Greti},
    title={Medieval Latin},
    booktitle={A Companion to the Latin Language},
    editor={Clackson, James},
    publisher={Blackwell Publishing},
    year={2011},
    pages={284--302},
    doi={10.1002/9781444343397},
    url={https://onlinelibrary.wiley.com/doi/book/10.1002/9781444343397},
}

@data{PEJU8B_2022,
author = {Bastianello, Elisa and Adamou, Alessandro and Grundig, Christine and Gabay, Simon and Phillip Benjamin Ströbel, Phillip and Kupreyev, Maxim N. and Solonets, Polina and Thoden, Klaus and Agostini, Giorgia and Cowey, James M.S. and Mühlberger, Günter and Mansutti, Sara and Kampkaspar, Dario and Chestnova, Elena and Degl'Innocenti, Emiliano and Di Meo, Carmen and Spadi, Alessia and Spinelli, Federica and Coradeschi, Francesco and Sanesi, Maurizio and Canova, Leonardo and Delle Fave, Damiano and Peroni, Silvio and Baumgartner, Reto and Meier, Wolfgang and Sander, Christoph and Bennett, Mike and Turska, Magdalena},
publisher = {Edmond},
title = {{Digital Publishing for the Humanities - New Technologies and Ideas}},
year = {2022},
version = {V1},
doi = {10.17617/3.PEJU8B},
url = {https://doi.org/10.17617/3.PEJU8B}
}

@online{nationalarchives, 
    author={The National Archives},
    title={Latin for Beginners: {Lesson} 19 - Participles (Present, Past, and Future)},
    year={2017},
    url={https://www.nationalarchives.gov.uk/latin/stage-2-latin/lessons/lesson-19-participles-present-past-and-future/},
    note={Accessed: 27.11.2023},
}

@online{universaldependencies, 
    author={Universal Dependencies},
    title={Universal Dependencies {(UD)} Documentation: {VerbType}},
    year={2022},
    url={https://universaldependencies.org/u/feat/VerbType.html},
    note={Accessed: 27.11.2023},
}

@book{leonhardt2013latin,
  title={Latin: Story of a world language},
  author={Leonhardt, J{\"u}rgen},
  year={2013},
  publisher={Harvard University Press},
  address={Cambridge, Massachusetts}
}

@book{allen1989vox,
  title={Vox latina},
  author={Allen, William Sidney},
  year={1989},
  publisher={Cambridge University Press}
}

@misc{BullingerDigital2020-1,
    title = {Bullinger {Digital}},
    author = {Bullinger{ }Digital},
    copyright = {Institut für Computerlinguistik und Institut für Schweizerische Reformationsgeschichte der Universität Zürich},
    url = {https://www.bullinger-digital.ch},
    urldate = {2023-10-13},
    year = {2023},
}

@article{hochreiter1997long,
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@misc{montani_explosionspacy_2023,
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@inproceedings{schmid1994probabilistic,
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@inproceedings{schmid1995improvements,
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@article{gpt3,
  author       = {Tom B. Brown and
                  Benjamin Mann and
                  Nick Ryder and
                  Melanie Subbiah and
                  Jared Kaplan and
                  Prafulla Dhariwal and
                  Arvind Neelakantan and
                  Pranav Shyam and
                  Girish Sastry and
                  Amanda Askell and
                  Sandhini Agarwal and
                  Ariel Herbert{-}Voss and
                  Gretchen Krueger and
                  Tom Henighan and
                  Rewon Child and
                  Aditya Ramesh and
                  Daniel M. Ziegler and
                  Jeffrey Wu and
                  Clemens Winter and
                  Christopher Hesse and
                  Mark Chen and
                  Eric Sigler and
                  Mateusz Litwin and
                  Scott Gray and
                  Benjamin Chess and
                  Jack Clark and
                  Christopher Berner and
                  Sam McCandlish and
                  Alec Radford and
                  Ilya Sutskever and
                  Dario Amodei},
  title        = {Language Models are Few-Shot Learners},
  journal      = {CoRR},
  volume       = {abs/2005.14165},
  year         = {2020},
  url          = {https://arxiv.org/abs/2005.14165},
  eprinttype    = {arXiv},
  eprint       = {2005.14165},
  timestamp    = {Thu, 25 May 2023 10:38:31 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2005-14165.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@misc{weischedel2013ontonotes,
  title={Ontonotes release 5.0},
  author={Weischedel, Ralph and Pradhan, Sameer and Ramshaw, Lance and Palmer, Martha and Xue, Nianwen and Marcus, Mitchell and Taylor, Ann and Houston, Ann and Hovy, Eduard and Belvin, Robert and El-Bachouti, Mohammed and Franchini, Michelle},
  year={2013},
  doi={https://doi.org/10.35111/xmhb-2b84}
}