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

本研究提出了一种基于 N-Gram 与 Damerau Levenshtein 距离模型的自动规则抽取方法，这种模型利用少量的数据进行训练，实现了良好的性能，并且在没有足够数据时，传统的方法胜过复杂的深度学习模型。