Jabberwocky: Using Context Clues to Find Meaning in ‘Nonsensical Speech’

No matter what your preferred method of counting is, the average adult knows a very large number of words. Moreover, most of these words were not—and could not have been—explicitly taught to the individual. Contextual Vocabulary Acquisition (CVA) is an explanation via computational model of how these underspecified terms slip into our daily vocabulary and how to help people actively acquire them. It is instantiated in a knowledge representation, reasoning, and acting system called SNePS. We endow CASSIE, a SNePS-based agent, with the world knowledge required to understand a passage. As she is exposed to more of the text, we can query her regarding new inferences about and information gained from the reading. One advantage of CVA over other models is that CASSIE’s need for an explicit and computable algorithm forces us to fully define all facets of our theory. I will analyze CASSIE’s ability to garner information from Lewis Carroll’s Jabberwocky, a poem famous for its ability to convey meaning despite the vast number of nonsense words. Specifically, I am interested in what CASSIE understands about a “jabberwock” from a context with as many novel as familiar words. The conclusion offers an analysis of CASSIE’s results in comparison to human interpretations of the same passage. Finally, I discuss methods for increasing CASSIE’s understanding of the poem and how to streamline her prior knowledge.