Detecting Clitics Related Orthographic Errors in Turkish

RANLP 2019  ·  Ugurcan Arikan, Onur Gungor, Suzan Uskudarli ·

For the spell correction task, vocabulary based methods have been replaced with methods that take morphological and grammar rules into account. However, such tools are fairly immature, and, worse, non-existent for many low resource languages... Checking only if a word is well-formed with respect to the morphological rules of a language may produce false negatives due to the ambiguity resulting from the presence of numerous homophonic words. In this work, we propose an approach to detect and correct the {``}de/da{''} clitic errors in Turkish text. Our model is a neural sequence tagger trained with a synthetically constructed dataset consisting of positive and negative samples. The model{'}s performance with this dataset is presented according to different word embedding configurations. The model achieved an F1 score of 86.67{\%} on a synthetically constructed dataset. We also compared the model{'}s performance on a manually curated dataset of challenging samples that proved superior to other spelling correctors with 71{\%} accuracy compared to the second-best (Google Docs) with and accuracy of 34{\%}. read more

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