Exploiting Syntactic Similarities for Preposition Error Corrections on Indonesian Sentences Written by Second Language Learner
Budi Irmawati, Hiroyuki Shindo, Yūji Matsumoto · Procedia Computer Science · 2016
We propose a method to artificially generate training data to correct preposition errors in Indonesian sentences written by second language learners. Basically, we injected large size of native sentences with preposition errors learned from learners’ sentences. Our method copies a preposition error from a learner sentence to a native sentence by firstly calculating a syntactic similarity score between the native sentence and the learners’ sentence. Then, it chooses the preposition error from the learner sentence that has the highest syntactic similarity score to the native sentence, to replace the original preposition in the native sentence. Experimental results show that the preposition error correction model trained on the artificial data resulted from our method outperforms the correction model trained on the similar size of native data.