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

• URL: http://arxiv.org/abs/2609.06266v1
• Date: Sat, 05 Sep 2026 21:23:35 GMT
• Title: Correction as Annotation: Bootstrapping a Dependency Parser for Documentary Medieval Latin
• Abstract summary: Medieval documentary sources remain inadequately served by natural language processing tools.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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