Spelling normalisation of Late Modern English: comparison and combination of VARD and character-based statistical machine translation

Abstract

To be able to profit from natural language processing (NLP) tools for analysing historical text, an important step is spelling normalisation. We first compare and second combine two different approaches: on the one hand VARD, a rule-based system which is based on dictionary lookup and rules with non-probabilistic but trainable weights; on the other hand a language-independent approach to spelling normalisation based on statistical machine translation (SMT) techniques. The rule-based system reaches the best accuracy, up to 94% precision at 74% recall, while the SMT system improves each tested period. We obtain the best system by combining both approaches. Re-training VARD on specific time-periods and domains is beneficial, and both systems benefit from a language sequence model using collocation strength.