Metonymy is a figure of speech in which one item’s name represents another item that usually has a close relation with the first one. Metonymic expressions need to be correctly detected and interpreted because sentences including such expressions have different mean- ings from literal ones; computer systems may output inappropriate results in natural language processing. In this paper, an associative approach for analyzing metonymic expressions is proposed. By using associative information and two conceptual distances between words in a sentence, a previous method is enhanced and a decision tree is trained to detect metonymic expressions. After detecting these expressions, they are interpreted as metonymic understanding words by using associative information. This method was evaluated by comparing it with two baseline methods based on previous studies on the Japanese language that used case frames and co-occurrence information. As a result, the proposed method exhibited significantly better accuracy (0.85) of determining words as metonymic or literal expressions than the baselines. It also exhibited better accuracy (0.74) of interpreting the detected metonymic expressions than the baselines.

Natural language processing technology has developed remarkably, but it is still difficult for computers to understand contextual meanings as humans do. The purpose of our work has been to construct an associative concept dictionary for Japanese verbs and make computers understand contextual meanings with a high degree of accuracy. We constructed an automatic system that can be used to estimate elliptical words. We present the result of comparing words that were estimated both by our proposed system (VNACD) and three baseline systems (VACD, NACD, and CF). We then calculated the mean reciprocal rank (MRR), top N accuracy (top 1, top 5, and top 10), and the mean average precision (MAP). Finally, we showed the effectiveness of our method for which both an associative concept dictionary for verbs (Verb-ACD) and one for nouns (Noun-ACD) were used. From the results, we conclude that both the Verb-ACD and the Noun-ACD play a key role in estimating elliptical words.