著者

岩山 真 徳永 健伸 田中 穂積 Makoto Iwayama Takenobu Tokunaga Hozumi Tanaka

雑誌

人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence (ISSN:09128085)

巻号頁・発行日

vol.6, no.5, pp.674-681, 1991-09-01

---

This paper presents a computational model for understanding metaphors using the measure of salience. Understanding metaphors is a property transfer process from a source concept to a target concept. There are two questions arise in the transfer process. One is which properties are more likely transferred from the source concept to the target concept, and other is the representation of highlighting the transferred properties. We use the measure of salience to answer these questions. The measure of salience represents how typical or prominent a property is. In understanding metaphors, typical properties are easy to transferred from a source concept to a target concept, so the measure of salience can measure the transferability of properties. And, the transferred properties become typical properties in the target concept, so highlighting the properties can be represented by increasing the measure of salience. For now, many researches have used the measure of salience in the process of understanding metaphors^^<(l)-(4)>, but they have not described precisely how the measure of salience is calculated. This paper presents the method of calculating the measure of salience based on the information theory. We use the redundancy of a property and the distribution of the redundancy among similar conceps. We think this method meet well with the human's intuition.