A Method for Classifying Onomatopoeia’s Senses Using BERT’s Contextual Information

Abstract

Many kinds of onomatopoeia in Japanese have multiple meanings: “gorogoro” is used in a different sense, such as “thunder rumbling” and “chilling out at home.” From previous researches, it is known that determining the meaning of onomatopoeia is depend on the dependent verbs. However, there are also onomatopoeia without verbs. Additionally, the meaning determination may require extensive contextual information. In this paper, we examine the possibility of senses classification considering the appearance context of onomatopoeia using BERT pre-trained model, which is a general-purpose language model. The evaluation results of sense classification show that accuracies of “gorogoro” and “batabata” are 73.9% and 57.8% respectively. Although the classification performance was significantly different by onomatopoeia’s senses, the performance of senses which was included more in the training data was good.