Machine Learning Approach for Correcting Preposition Errors using SVD Features

Non-native English writers often make preposition errors in English language. The most commonly occurring preposition errors are preposition replacement, preposition missing and unwanted preposition. So, in this method, a system is developed for finding and handling the English preposition errors in preposition replacement case. The proposed method applies 2-Singular Value Decomposition (SVD2) concept for data decomposition resulting in fast calculation and these features are given for classification using Support Vector Machines (SVM) classifier which obtains an overaU accuracy above 90%. Features are retrieved using novel SVD2 based method applied on trigrams which is having a preposition in the middle of the context. A matrix with the left and right vectors of each word in the trigram is computed for applying SVD2 concept and these features are used for supervised classification. Preliminary results show that this novel feature extraction and dimensionality reduction method is the appropriate method for handling preposition errors.