Computational approaches to analogical reasoning: a comparative analysis
Artificial Intelligence
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
A knowledge representation approach to understanding metaphors
Computational Linguistics
Metonymy and metaphor: what's the difference?
COLING '88 Proceedings of the 12th conference on Computational linguistics - Volume 1
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This paper presents a computaional method of calculating the measure of salience in understanding metaphors. We mainly treat metaphors in the form of "A is (like) B," in which "A" is called target concept, and "B" is called source concept. In understanding a metaphor, some properties of the source concept are transferred to the target concept. In the transfer process, we first have to select the properties of the source concept that can be more preferably transferred to the target concept. The measure of salience represents how typical or prominent the property is and is used to measure the transferability of the property. By introducing the measure of salience, we have to consider only the high salient properties after the selection. The measure of salience was calculated from Smith & Medin's probabilistic concept[12, 13] according to Tversky's two factors[14]. One is intensity which refers to signal-to-noise ratio; this is calculated from the entropy of properties. The other is diagnostic factor which refers to the classificatory significance of properties; this is calculated from the distribution of the property's intensity among similar concepts. Finally we briefly outline the whole process of understanding metaphors using the measure of salience.