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In recent years, Bayesian models have become increasingly popular as a way of understanding human cognition. Ideal learner Bayesian models assume that cognition can be usefully understood as optimal behavior under uncertainty, a hypothesis that has been supported by a number of modeling studies across various domains The models in these studies aim to(More)
The frequent occurrence of divergences|structural diier-ences between languages|presents a great challenge for statistical word-level alignment. In this paper, we introduce DUSTer, a method for systematically identifying common divergence types and transforming an English sentence structure to bear a closer resemblance to that of another language. Our(More)
Subtle social information is available in text such as a speaker's emotional state, intentions, and attitude, but current information extraction systems are unable to extract this information at the level that humans can. We describe a methodology for creating databases of messages annotated with social information based on interactive games between humans(More)
We describe a new supervised machine learning approach for detecting authorship deception, a specific type of authorship attribution task particularly relevant for cybercrime forensic investigations, and demonstrate its validity on two case studies drawn from realistic online data sets. The core of our approach involves identifying uncharacteristic behavior(More)
We use historical change to explore whether children filter their input for language learning. Although others (e.g., Rohde & Plaut, 1999) have proposed filtering based on string length, we explore two types of filters that assume richer linguistic structure. One presupposes that linguistic utterances are structurally highly ambiguous and focuses learning(More)
Language acquisition is a problem of induction: the child learner is faced with a set of specific linguistic examples and must infer some abstract linguistic knowledge that allows the child to generalize beyond the observed data, i.e., to both understand and generate new examples. Many different generalizations are logically possible given any particular(More)
1. Introduction Word segmentation is one of the first problems infants must solve during language acquisition, where words must be identified in fluent speech. A number of weak cues to word boundaries are present in fluent speech, and there is evidence that infants are able to use many of these, including phonotactics However, with the exception of the last(More)
The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be(More)