Learning, Using Examples, to Translate Phrases and Sentences to Meanings
Adam Davis Kraft · 2007
Before we can create intelligent systems that exhibit the versatility of the human intellect, we must understand how our command of language enables the uniquely broad scope of our reasoning ability. To pursue such an understanding, we must discover the ways by which language gives rise to representations which, in turn, serve as the building blocks of models that capture constraints and regularities of our environment. The work described in this thesis constitutes a step toward this goal. I have combined aspects of Winston's Arch-Learning methodology with implementations of three powerful representations: Lexical Conceptual Semantics[Jackendoff 1983], Transition Spaces[Borchardt 1993], and Thread Memory[Vaina, Greenblatt 1979], in a system that learns to instantiate semantic descriptions from language based on a sequence of examples. My program, Lance, builds