Stevens, Jon

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2012 - 20142
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  • Publication
    Using salience and hypothesis evaluation to learn object names in real time
    (2012-05-01) Stevens, Jon
    This paper presents a computational model of word learning that has roots in experimental literature and learns in real time with high precision from a small amount of data. In addition to incorporating external cues a la Yu & Ballard 2007, we give the learner the ability to test specific highly probable semantic hypotheses against new data. Performance is comparable to that of a more complex model (Frank et al. 2009) and better than that of a similar model (Fazly et al. 2010) that does not utilize hypothesis evaluation.
  • Publication
    How Uniqueness Guides Definite Description Processing
    (2014-03-28) Ahern, Christopher A; Stevens, Jon
    Most analyses of definiteness are based on two important notions: uniqueness and familiarity. Fundamentally, both approaches ascribe some content to the conventional meaning of definite, but not indefinite, descriptions. We explore the effect of determiner choice on listeners’ expectations about possible referents using eye-tracking in the visual world paradigm. We present listeners with temporarily ambiguous definite descriptions where a single referent is unique under the greatest number of possible semantic descriptions. We find that uniqueness is not only a robust notion for describing the meaning of definitess, but also a crucial factor in guiding listeners in the online processing of definite descriptions.