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With 84.75 million Filipinos online, the ability for models to process onlinetext is crucial for developing Filipino NLP applications. To this end, spellingcorrection is a crucial preprocessing step for downstream processing. However,the lack of data prevents the use of language models for this task. In thispaper, we propose an N-Gram + Damerau Levenshtein distance model with automaticrule extraction. We train the model on 300 samples, and show that despitelimited training data, it achieves good performance and outperforms other deeplearning approaches in terms of accuracy and edit distance. Moreover, the model(1) requires little compute power, (2) trains in little time, thus allowing forretraining, and (3) is easily interpretable, allowing for directtroubleshooting, highlighting the success of traditional approaches over morecomplex deep learning models in settings where data is unavailable.

Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in Filipino