You can’t suggest that?!

Abstract

In this article, we study correction of spelling errors, specifically on how the spelling errors are made and how can we model them computationally in order to fix them.The article describes two different approaches to generating spelling correction suggestions for three Uralic languages: Estonian, North Sámi and South Sámi.The first approach of modelling spelling errors is rule-based, where experts write rules that describe the kind of errors are made, and these are compiled into finite-state automaton that models the errors.The second is data-based, where we show a machine learning algorithm a corpus of errors that humans have made, and it creates a neural network that can model the errors.Both approaches require collection of error corpora and understanding its contents; therefore we also describe the actual errors we have seen in detail.We find that while both approaches create error correction systems, with current resources the expert-build systems are still more reliable.

GiellaLT: an infrastructure for rule-based language technology tool development

“…Spellcheckers are based on weighted finite state technology as described by (Pirinen and Lindén, 2014). There is also support for neural network based models of spellchecking (Kaalep et al, 2022), this is however in its current stage still not up to par with the traditional weighted finitestate models given the current error corpus sizes. Since 2019 the GiellaLT infrastructure sup ports building grammar checkers (Wiechetek et al, 2019a) and these are available for some of the Sámi languages already.…”