Learning grounded semantics with word trees: Prepositions and pronouns

The authors present a method by which a robot can learn the meanings of words from unlabeled correct examples in context. The "word trees" method consists of reconstructing the speaker's decision process in choosing a word. The facts about an object and its relation to other objects that maximally reduce the uncertainty (entropy) of word choice become (he decision nodes of this tree. The conjunction of the choices leading to a word becomes its logical definition. Definitions thereby become only as complex as is necessary to distinguish words in the vocabulary, making the method appear to follow a heuristic that developmental psychologists call the "Principle of Contrast." Combined with a method for inferring word type and reference, the method produces semantics complete enough to produce or understand full sentences. The method was implemented on a robot with visual, auditory, and positional sensors, and succeeded in learning the differences between "I," "you," "he," "this," "that," "above," "below," and "near."