Spell Correction for Azerbaijani Language using Deep Neural Networks
This work provides a spell correction solution for the Azerbaijani language, which is challenging due to its complex morphological structure.
This paper addresses spell correction for the morphologically complex Azerbaijani language using a sequence-to-sequence model with an attention mechanism. The model, trained on 12,000 sentence pairs, achieved F1-scores of 75% for distance 0, 90% for distance 1, and 96% for distance 2 on 1,000 real-world misspelled words.
Spell correction is used to detect and correct orthographic mistakes in texts. Most of the time, traditional dictionary lookup with string similarity methods is suitable for the languages that have a less complex structure such as the English language. However, the Azerbaijani language has a more complex structure and due to its morphological structure, the derivation of words is plenty that several words are derived from adding suffices, affixes to the words. Therefore, in this paper sequence to sequence model with an attention mechanism is used to develop spelling correction for Azerbaijani. Total 12000 wrong and correct sentence pairs used for training, and the model is tested on 1000 real-world misspelled words and F1-score results are 75% for distance 0, 90% for distance 1, and 96% for distance 2.