A Method for Detecting Overgeneralized Be-Verb based on Subject-compliment Identification

Abstract

This paper addresses the detection of overgeneralization of be-verb found in learner English. It is an error where the subject and complement are not semantically equivalent in a be-verb sentence as in *Paris is rain. This paper presents a method for detecting it by predicting through word embeddings whether a given subject and complement pair is semantically equivalent or not. This paper also presents a method for determining the hyperparameters in the method efficiently and effectively. Experiments show that the present method outperforms two baseline methods based on corpus statistics and WordNet ontology. Looking into the detection results brings out a way of generating feedback messages for learners that explain why the detected error is not a valid English expression.