While some of the underlying structures of the metaphors -- the conceptual categories -- are near universal (e.g. Happy Is Up), there are many variations in their range, elaboration, and emphasis. And, of course, not every category is universal. For example, Kövecses points to a special conceptual category in Japanese centered around the hara, or belly, "Anger Is (In The) Hara." In Zulu, one finds an important category, "Anger Is (Understood As Being) In the Heart," which would be rare in English. Alternatively, while many cultures conceive of anger as a hot fluid in a container, it's in English that we "blow off steam," a turn of phrase that wouldn't make sense in Zulu.

These relationships have been painstakingly mapped by human analysts over the last 30 years and they represent a deep culturolinguistic knowledge base. For the cognitive linguistic school, all of these uses of language reveal something about the way the people of a culture understand each other and the world. And that's really the target of the metaphor program, and what makes it unprecedented. They're after a deeper understanding of the way people use words because the deep patterns encoded in language may help intelligence analysts understand the people, not just the texts.

For Lakoff, it's about time that the government started taking metaphor seriously. "There have been 30 years of neglect of current linguistics in all government-sponsored research," he told me. "And finally there is somebody in the government who has managed to do something after many years of trying."

UC San Diego's Bergen agreed. "It's a totally unique project," he said. "I've never seen anything like it."

But that doesn't mean it's going to be easy to create a system that can automatically deduce what Americans' biases about education from a statement like "The teacher spoon-fed the students."

Lakoff contends that it will take a long, sustained effort by IARPA (or anyone else) to complete the task. "The quick-and-dirty way" won't work, he said. "Are they going to do a serious scientific account?"

Building a Metaphor Machine



The metaphor problem is particularly difficult because we don't even know what the right answers to our queries are, Bergen said.

"If you think about other sorts of automation of language processing, there are right answers," he said. "In speech recognition, you know what the word should be. So you can do statistical learning. You use humans, tag up a corpus and then run some machine learning algorithms on that. Unfortunately, here, we don't know what the right answers are."

For one, we don't really have a stable way of telling what is and what is not metaphorical language. And metaphorical language is changing all the time. Parsing text for metaphors is tough work for humans and we're made for it. The kind of intensive linguistic analysis that's made Lakoff and his students (of whom Bergen was one) famous can take a human two hours for every 500 words on the page.