Neural Architectures for Old English Lemmatisation: A Comparative Study of Corpus-Based and Dictionary-Augmented Approaches

Resumen

Lemmatisation is an essential step in the computational analysis of historical corpora, such as Old English, which presents problems of spelling variation, widespread inflectional morphology, and limited annotated resources. This paper conducts a detailed comparison of neural architectures for Old English lemmatisation: encoder-decoder (BiLSTM with attention) and Transformer under three experimental conditions (corpus-only, dictionary augmentation without POS tags, with POS tags) and advanced techniques (character-level augmentation, learning cycles with restarts). The Transformer achieves 66.65% development accuracy and our improved model 67.58% test accuracy. Dictionary augmentation without POS degrades performance (?53% unknown POS); with POS it recovers but does not exceed corpus-only training. Pre-trained embeddings (Word2Vec) and BPE tokenization reduce accuracy (up to ?12.67% and ?12.92%), showing that lemmatisation is a morphological task where semantic signals introduce noise.