"This work brings opportunities toward modeling metaphorical words at a broad scale, ultimately allowing the construction of artificial intelligence systems that are capable of creating and comprehending metaphorical language," he added.
Srinivasan and Xu conducted the study with Lehigh University psychology professor Barbara Malt.
Using the Metaphor Map of English database, researchers examined more than 5,000 examples from the past millennium in which word meanings from one semantic domain, such as "water," were extended to another semantic domain, such as "mind."
Researchers called the original semantic domain the "source domain" and the domain that the metaphorical meaning was extended to, the "target domain."
More than 1,400 online participants were recruited to rate semantic domains such as "water" or "mind" according to the degree to which they were related to the external world (light, plants), animate things (humans, animals), or intense emotions (excitement, fear).
These ratings were fed into computational models that the researchers had developed to predict which semantic domains had been the sources or targets of metaphorical extension.
In comparing their computational predictions against the actual historical record provided by the Metaphor Map of English, researchers found that their models correctly forecast about 75 percent of recorded metaphorical language mappings over the past millennium.
Furthermore, they found that the degree to which a domain is tied to experience in the external world, such as "grasping a rope," was the primary predictor of how a word would take on a new metaphorical meaning such as "grasping an idea."
For example, time and again, researchers found that words associated with textiles, digestive organs, wetness, solidity and plants were more likely to provide sources for metaphorical extension, while mental and emotional states, such as excitement, pride and fear were more likely to be the targets of metaphorical extension.
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