Correction as Annotation: Bootstrapping a Dependency Parser for Documentary Medieval Latin

Abstract:Medieval documentary sources remain inadequately served by existing natural language processing tools. None of the five readily available Latin treebank models attains usable performance on a collection of 160 inventories compiled in Marseille between 1258 and 1446. The best labelled attachment score is 0.62 and the best morphology-aware score is 0.24. Performance does not correlate with either genre or period proximity. To address this shortfall, in-domain training data was generated as a by-product of using these inadequate models. In each of nine iterations, a model pre-annotated 200 sentences; an expert corrected the annotations; and the corrected sentences were used to train the subsequent model, with batches sampled independently of model state, without active-learning selection. Thirty-three hours of annotation effort over 1,804 sentences increased universal part-of-speech accuracy from 0.80 to 0.98 and labelled attachment from 0.48 to 0.92, outperforming all baselines on the reported metrics while using 97% less training data than the largest one of them. Annotator effort declined from 54% of tokens to a plateau of 14-18%, an operational progress metric that requires no separate gold standard and can serve as a stopping criterion.

40 pages, 6 figures, 10 tables. Code and data: this https URL . Trained models archived at this https URL

Computation and Language (cs.CL); Digital Libraries (cs.DL)

arXiv:2609.06266 [cs.CL]

(or arXiv:2609.06266v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.06266 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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