Japanese onomatopoeias are very difficult for machines to recognize and translate into other languages due to their uniqueness. In particular, onomatopoeias that convey several meanings are very confusing for machine translation systems to distinguish and translate correctly. In this paper, we discuss what features are helpful in order to automatically disambiguate the meaning of onomatopoeias that have two different meanings. We used nouns, adjectives, and verbs extracted from sentences as features, then carried out a machine learning classification analysis and compared the accuracy of how well these features differentiate two meanings of ambiguous onomatopoeias. As a result, we discovered that employing a combination of machine learning with nouns and verbs as a feature achieved accuracy of above 80 points. In addition, we were able to improve the accuracy by excluding pronouns and proper nouns and also by limiting verbs to those that are modified by onomatopoeias. In future, we plan to concentrate on dependency between verbs that are modified by onomatopoeia and nouns, as we believe that this approach will help machine translation to translate Japanese onomatopoeias correctly.
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