Language comprehension requires a complex series of decisions under uncertainty. This is especially obvious when one string may have multiple different interpretations, whether due to lexical ambiguity, or the potential for an inference beyond literal content. This dissertation profiles how the human system for language comprehension times those…

Much recent work in natural language processing treats linguistic analysis as an inference problem over graphs. This development opens up useful connections between machine learning, graph theory, and linguistics. The first part of this dissertation formulates syntactic dependency parsing as a dynamic Markov random field with the novel…

Inferences are important because not everything in the text is explicit. Therefore, the reader must generate inferences that fill in "missing" information. Various factors can influence inference processes, including those that are related to the text and reader. Moreover, these two factors are likely to interact in highly complex ways,…