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A.1 Links to published Latin word embedding models
- Bamman: https://www.cs.cmu.edu/~dbamman/latin.html
- Grave et al. (2018): https://fasttext.cc/docs/en/crawl-vectors.html
- Sprugnoli et al. (2019) (word2vec CBOW): ("allLASLAlemmi-vector-100-nocase-w5-CBOW.doc"), https://embeddings.lila-erc.eu/samples/download/word2vec
- Sprugnoli et al. (2019) (FastText SG): ("allLASLA-lemmi-fast-100-SKIP-win5-min5 vec"), https://embeddings.lila-erc.eu/samples/download/fasttext
- Bloem et al. (2020): ("bamman-c50-d100"), https://github.com/bloemj/nonce2vec/tree/nonce2vec-latin
A.2 Training details for new Latin word2vec models
- word2vec (CLTK) is trained using Gensim word2vec with the following parameters: size  $= 300$ , min_count  $= 100$ , iter  $= 1$ . The model is trained on plaintext files from Bamman (https://www.cs.cmu.edu/~dbamman/latin.html) lemmatized with CLTK BackoffLatinLemmatizer (Johnson, 2021).
- word2vec (TT) is trained using Gensim word2vec with the following parameters: size = 50, min_count = 100, iter = 1. The model is trained on plaintext files from Bamman (https://www.cs.cmu.edu/~dbamman/latin.html) lemmatized with TreeTagger (Schmid, 1994).
A.3 Links to publicly available datasets
- Synonym selection dataset from Sprugnoli et al. (2019): https://embeddings.lila-erc.eu/samples/syn/syn-selection-benchmark-Latin.tsv
- Dictionary of Latin near-synonyms from Spinelli (2018): https://github.com/tommasospinelli/Online-Dictio
nary-of-Latin-Near-Synonyms/ blob/master/Latin%20Near-Synonyms%20dataset.txt
- Database of possible citational passages in Livy from Dexter et al. (2017): https://github.com/qcrit/PNAS_2017_QuantitativeCriticism/blob/master/Code/LivyPassages.csv
A.4 Parameters for Tesserae searches
We run four Tesserae searches comparing Book 1 of Valerius Flaccus' Argonautica to Vergil's Aeneid, Ovid's Metamorphoses, Lucan's Pharsalia, and Statius' Thebaid, respectively. For these searches, we use version 3 of the online Tesserae tool (http://tesserae.caset buffalo.edu/) with all parameters set to their default values: unit = line, feature = lemma, number of stop words = 10, stoplist basis = corpus, score basis = word, frequency basis = corpus, maximum distance = 10 words, distance metric = frequency, drop scores below = 6.
A.5 Latin texts
The texts of the Latin epic poems included in our intertextual search analysis are from the Tesserae corpus ( https://github.com/tesserae/tesserae/tree/master/texts), which is derived from the Perseus Digital Library ( http: //www.perseus.tufts.edu/hopper/).
Following Dexter et al. (2017), we consider 17 texts for the stylistic analysis of Livy, abbreviated in Fig. 2 according to the Oxford Classical Dictionary: Agr, Cato's De agri cultura; Ann, Tacitus' Annals; Conf, Augustine's Confessions; De or, Cicero's De oratore; De rep, Cicero's De republica; Cat, Sallust's De coniuratione Catilinae; G, Vergil's Georgics; Gal, Caesar's Bellum Gallicum; Ger, Tacitus' Germania; HF, Seneca's Hercules Furens; Inst 1, Quintilian's Institutio Oratoria 1; Iug, Sallust's Bellum Iugurthinum; Lucr, Lucretius' De rerum natura; Mur, Cicero's Pro Murena; Ps, Plautus' Pseudolus; Theb, Statius' Thebaid; Vitr, Vitruvius' De architectura. Further information about this corpus is available in Dexter et al. (2017).