Data-Driven Spelling Correction using Weighted Finite-State Methods
Miikka Silfverberg, Pekka Kauppinen, Krister Lindén · 2016
This paper presents two systems for spelling correction formulated as a sequence labeling task. One of the systems is an unstructured classifier and the other one is structured. Both systems are implemented using weighted finite-state methods. The structured system delivers stateof-the-art results on the task of tweet normalization when compared with the recent AliSeTra system introduced by Eger et al. (2016) even though the system presented in the paper is simpler than AliSeTra because it does not include a model for input segmentation. In addition to experiments on tweet normalization, we present experiments on OCR post-processing using an Early Modern Finnish corpus of OCR processed newspaper text.