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Lecture7-WordMeaningMapping1

Lecture7-WordMeaningMapping1 - Computational Problem Psych...

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Psych 215L: Language Acquisition Lecture 7 Word-Meaning Mapping “I love my daxes.” Dax = that specific toy, teddy bear, stuffed animal, toy, object, …? Computational Problem Xu & Tenenbaum (2007) Previous approaches to word-learning: Hypothesis elimination: hypothesis space of potential concepts for word exists and learner eliminates incorrect hypotheses based on input (Pinker 1984, 1989, Berwick 1986, Siskind 1996) Associative learning: connectionist networks (Colunga & Smith, 2005; Gasser & Smith, 1998; Regier, 1996, 2005; L. B. Smith, 2000) or similarity matching to examples (Landau, Smith, & Jones, 1988; Roy & Pentland, 2004) – no explicit hypothesis space, per se Xu & Tenenbaum (2007) 5 things a word-learning model should do: (1)Word meanings learned from very few examples (2)Word meanings inferred form only positive examples (3)The target of word-learning is a system of overlapping concepts (4)Inferences about word meaning based on examples should be graded, rather than absolute (5)Inferences about word meanings can be strongly affected by reasoning about how the observed examples were generated
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Xu & Tenenbaum (2007) Approach to word learning based on rational statistical inference (ideal learner) Hypothesis about word meanings evaluated by Bayesian probability theory Claim: “The interaction of Bayesian inference principles with appropriately structured hypothesis spaces can explain the core phenomena listed above. Learners can rationally infer the meanings of words that label multiple overlapping concepts, from just a few positive examples. Inferences from more ambiguous patterns of data lead to more graded and uncertain patterns of generalization. Pragmatic inferences based on communicative context affect generalizations about word meanings by changing the learner’s probabilistic models.” Ruling out unnatural extensions dog = dog parts, front half of dog, dog spots, all spotted things, all running things, all dogs + one cat Traditional Solutions: Whole Object constraint : First guess is that a label refers to a whole object, rather than part of the object ( dog parts, front half of dog ) or an attribute of the object ( dog spots ) Taxonomic constraint (Markman 1989): First guess about an unknown label is that it applies to the taxonomic class (ex: dog , instead of all running things or all dogs + one cat ) The issue of overlapping hypotheses Object-kind labels : dog vs. dalmatian vs. animal Issue: clearly overlapping labels – a dalmatian is a dog and an animal, but not all animals are dogs, and not all dogs are dalmatians. Which level does each label apply to? The issue of overlapping hypotheses Multiple properties potentially relevant : shape vs. material Issue: clearly overlapping labels – which aspect is being labeled?
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Traditional solutions For object-kind labeling : Markman (1989): learners prefer the “basic” level of categorization ( dog over dalmatian or animal ) Remaining issue: How do learners figure out non-basic level labels? That is, how do they overcome this bias? Since concepts are overlapping, it’s not enough to learn that dog can label a dog. Learners must
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