In previous studies, detecting metonymies has been done mainly by taking one of the following two approaches: rule-based approach and statistical one. The former uses semantic networks and rules to interpret metonymy. The latter uses corpus-based metonymy resolution with machine learning techniques.

One of the problems of the current metonymy detection is that using mainly syntactic and semantic information may not be enough to detect metonymic expressions because it has been pointed out that metonymic expressions have relations to associative relations between words.

In this paper, we propose an associative approach for detecting them. By using associative information between words in a sentence, we train a decision tree to detect metonymic expressions in a sentence. We evaluated our method by comparing with four baseline methods based on previous studies that use a thesaurus or co-occurrence information. Experimental results show that our method has significantly better accuracy (0.83) of judging metonymic expressions than those of the baselines. It also achieves better recall (0.73), precision (0.85), and F-measure (0.79) in detecting Japanese metonymic expressions, achieving state-of-the-art performance.