| Sumario: | We evaluate a Stanza-based pipeline for Old English that combines character-level processing with language-specific word embeddings derived from the Dictionary of Old English Corpus. On a 25,000-word dataset annotated with Universal Dependencies, character-level models yield consistent gains over token-based variants for lemmatization and dependency parsing (test UAS 88.92 per cent; LAS 79.65 per cent). Relative to a multilingual baseline for Old English, our best parser improves LAS by approximately 20 per cent; compared with a recent monolingual spaCy baseline on similar data sizes, gains are approximately 5 per cent on our split. We complement scores with qualitative analyses to illustrate strengths and limitations, and we provide implementation details to support replication. The main conclusion of this work is that, under low-resource conditions, language-specific embeddings and character-level modelling are more effective for Old English processing than cross-linguistic transfer learning.
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