1:50 PM - 2:10 PM
[3G1-01] A Method for Detecting Overgeneralized Be-Verb based on Subject-compliment Identification
Keywords:Grammatical error detection, Language learning assistance
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.
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.