Extracting implicit knowledge from text

Thesis (Ph. D.)--University of Rochester. Dept. of Computer Science and Dept. of Linguistics, 2010.The everyday intelligence of both humans and machines relies on a large store of background, or common-sense, knowledge. That such a knowledge base is not yet available to machines helps partially explain the community's inability to provide society with the sort of synthetic intelligence described by futurists such as Turing, or Asimov. In response, there have emerged a variety of methods for automated Knowledge Acquisition (KA) that are now being actively explored. Here I consider the extraction of knowledge that is conveyed implicitly, both within everyday texts and queries posed to internet search engines. Through recognizing certain forms of existential predicative patterns, and abstracting from these to more strongly quantifiable statements, I show that a significant amount of general knowledge can be gleaned based on how we talk about the world. I provide experimental results both for the direct extraction and strengthening of such knowledge, and for the automatic acquisition of supporting resources for this task. In addition, I draw attention to the relationship between automatically acquired background knowledge and natural language generic sentences. Humans use generics when they wish to directly assert the same sorts of \rules of the world" that are of concern to the KA community. And yet, there has been little recognition in applied circles that decades of work from formal linguistic semantics may have a role to play in the representation, and perhaps even the acquisition, of common knowledge