Models for natural language processing use statistics to collect a wealth of information about word meanings. In “Through the Looking Glass,” Humpty Dumpty says scornfully, “When I use a word, it means just what I choose it to mean — neither more nor less.” Alice replies, “The question is whether y
The study, which was published in the journal, demonstrates that artificial intelligence systems may really pick up on highly complex word meanings. The researchers also found a simple method for gaining access to this sophisticated information. They discovered that the AI system they looked at represents word meanings in a manner that closely resembles human judgment.
When words frequently occur together — “table” and “chair,” for example — the system learns that their meanings are related. And if pairs of words occur together very rarely — like “table” and “planet,” — it learns that they have very different meanings. Before the research began, Blank said, the system appeared to have one major limitation: “As far as the system is concerned, every two words have only one numerical value that represents how similar they are.”“Consider our knowledge of dolphins and alligators,” Blank said. “When we compare the two on a scale of size, from ‘small’ to ‘big,’ they are relatively similar. In terms of their intelligence, they are somewhat different.
Using that method, the scientists studied 52-word groups to see whether the system could learn to sort meanings — like judging animals by either their size or how dangerous they are to humans, or classifying U.S. states by weather or by overall wealth.
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