Building representations from natural language

Abstract

In this thesis, I describe a system I built that produces instantiated representations from descriptions embedded in natural language. For example, in the sentence 'The girl walked to the table', my system produces a description of movement along a path (the girl moves on a path to the table), instantiating a general purpose trajectory representation that models movement along a path. I demonstrate that descriptions found by my system enable the imagining of an entire inner world, transforming sentences into three-dimensional graphical descriptions of action. By building action descriptions from ordinary language, I illustrate the gains we can make by exploiting the connection between language and thought. I assert that a small set of simple representations should be able to provide powerful coverage of human expression through natural language. In particular, I examine the sorts of representations that are common in the Wall Street Journal from the Penn Treebank, providing a counterpoint for the many other sorts of analyses of the Penn Treebank in other work. Then, I turn to recognized experts in provoking our imaginations with words, using my system to examine the work of four great authors to uncover commonalities and differences in their styles from the perspective of the way they make representational choices in their work.