Grammatical Error Correction with Frequency-based Synthentic Corpus
Name
Jakob Univer
Abstract
In this thesis we introduce a grammatical error correction method with a neural network trained only on synthetic data. The method is useful for languages without big corpora for training a grammatical error correction model, like Estonian. From a smaller human corrected corpus, we found the probabilities of word deletion, addition, substitution and changing word order mistakes in the text. With the help of these probabilities we created a bigger synthetic corpus and we trained a neural network for grammatical error correction on the synthetic data. The author found that the probabilities of mistakes do not have to be very precise and the trained neural network can correct spelling mistakes as well as grammar mistakes.
Graduation Thesis language
Estonian
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Agnes Luhtaru, Mark FiĊĦel
Defence year
2022