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Sentence processing theories typically assume that the input to our language processing mechanisms is an error-free sequence of words. However, this assumption is an oversimplification because noise is present in typical language use (for instance, due to a noisy environment, producer errors, or perceiver errors). A complete theory of human sentence(More)
One of the most puzzling and important facts about communication is that people do not always mean what they say; speakers often use imprecise, exaggerated, or otherwise literally false descriptions to communicate experiences and attitudes. Here, we focus on the nonliteral interpretation of number words, in particular hyperbole (interpreting unlikely(More)
We investigate the effects of alternative utterances on pragmatic interpretation of language. We focus on two specific cases: specificity implicatures (less specific utterances imply the negation of more specific utterances) and Horn impli-catures (more complex utterances are assigned to less likely meanings). We present models of these phenomena in terms(More)
While the ubiquity and importance of nonliteral language are clear, people's ability to use and understand it remains a mystery. Metaphor in particular has been studied extensively across many disciplines in cognitive science. One approach fo-cuses on the pragmatic principles that listeners utilize to infer meaning from metaphorical utterances. While this(More)
We investigate the mechanisms that allow people to successfully understand language given noise in the world and in their own perceptual inputs. We address two parts of this question. First, what knowledge do people use to make sense of language inputs that may have been corrupted? Second, how much of this knowledge is used while people are processing(More)
We present a model for inducing sen-tential argument structure, which distinguishes arguments from optional modi-fiers. We use this model to study whether representing an argument/modifier distinction helps in learning argument structure, and whether a linguistically-natural argu-ment/modifier distinction can be induced from distributional data alone. Our(More)
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