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1
Developing semantic knowledge through cross-situational word learning
In: http://www.kachergis.com/docs/kachergis_yu_shiffrin_2014_priming.pdf (2014)
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2
Markers of Topical Discourse in Child-Directed Speech
In: http://langcog.stanford.edu/papers/RF-cogsci2014.pdf (2013)
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3
Actively learning object names across ambiguous situations
In: http://www.kachergis.com/docs/kachergis_etal2013_active.pdf (2013)
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4
Relating activity contexts to early word learning in dense longitudinal data
In: http://www.media.mit.edu/cogmac/publications/cogsci2012_RFR_fix.pdf (2012)
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5
Relating activity contexts to early word learning in dense longitudinal data
In: http://web.media.mit.edu/~dkroy/papers/pdf/cogsci2012_RFR.pdf (2012)
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6
Actively Learning Object Names Across Ambiguous Situations
In: http://www.indiana.edu/~dll/papers/kachergis2012_active_topics.pdf (2012)
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7
Functional biases in language learning: Evidence from word order and case-marking interaction
In: http://www.hlp.rochester.edu/publications/FedzechkinaJaegerNewport11-cogsci.pdf (2011)
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8
Functional biases in language learning: Evidence from word order and case-marking interaction
In: http://www.bcs.rochester.edu/people/newport/pdf/FedzechkinaJaegerNewport11.pdf (2011)
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9
Multilevel Exemplar Theory
In: http://www.ims.uni-stuttgart.de/%7Emoebius/papers/walsh_etal_cogsci2010.pdf (2010)
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10
Language acquisition meets language evolution
In: http://cnl.psych.cornell.edu/pubs/2010-cc-CogSci.pdf (2010)
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11
Indirect evidence and the poverty of the stimulus: The case of anaphoric one. Cognitive Science 33.287–300
In: http://psych.stanford.edu/~jlm/pdfs/ForakerEtAl09.pdf (2009)
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12
Indirect evidence and the poverty of the stimulus: The case of anaphoric one
In: http://linguistics.berkeley.edu/~regier/papers/foraker-et-al-2009.pdf (2009)
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13
From Exemplar to Grammar: A Probabilistic Analogy-based Model of Language Learning
In: http://staff.science.uva.nl/~rens/analogy.pdf (2009)
Abstract: While rules and exemplars are usually viewed as opposites, this paper argues that they form end points of the same distribution. By representing both rules and exemplars as (partial) trees, we can take into account the fluid middle ground between the two extremes. This insight is the starting point for a new theory of language learning that is based on the following idea: If a language learner does not know which phrase-structure trees should be assigned to initial sentences, s ⁄ he allows (implicitly) for all possible trees and lets linguistic experience decide which is the ‘‘best’ ’ tree for each sentence. The best tree is obtained by maximizing ‘‘structural analogy’ ’ between a sentence and previous sentences, which is formalized by the most probable shortest combination of subtrees from all trees of previous sentences. Corpus-based experiments with this model on the Penn Treebank and the Childes database indicate that it can learn both exemplar-based and rule-based aspects of language, ranging from phrasal verbs to auxiliary fronting. By having learned the syntactic structures of sentences, we have also learned the grammar implicit in these structures, which can in turn be used to produce new sentences. We show that our model mimicks children’s language development from item-based constructions to abstract constructions, and that the model can simulate some of the errors made by children
Keyword: Analogy; Auxiliary fronting; Computational modeling; Constructions; Data-oriented parsing (DOP; Discontiguous dependencies; Distituents; Language acquisition; Language generation; Poverty of the stimulus; Probabilistic context-free grammar; Probabilistic tree-substitution grammar; Rules versus exemplars; Statistical grammar induction; Unsupervised parsing; Unsupervised-DOP
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.8237
http://staff.science.uva.nl/~rens/analogy.pdf
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14
Artificial Language: Human Data and Modeling
In: http://leadserv.u-bourgogne.fr/IMG/pdf/CogSci2010.pdf (2009)
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15
Exploring word learning in a high-density longitudinal corpus
In: http://langcog.stanford.edu/papers/RFR-cogsci2009.pdf (2009)
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16
Exploring word learning in a high-density longitudinal corpus
In: http://141.14.165.6/CogSci09/papers/501/paper501.pdf (2009)
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17
Experience
In: http://www.languageinindia.com/march2009/multimediaponniah.pdf (2009)
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18
Continuity of discourse provides information for word learning
In: http://141.14.165.6/CogSci09/papers/312/paper312.pdf (2009)
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19
Continuity of discourse provides information for word learning
In: http://www.mit.edu/~ndg/papers/FrankGoodmanTenenbaumFernald2009.pdf (2009)
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20
Informative communication in word production and word learning
In: http://141.14.165.6/CogSci09/papers/280/paper280.pdf (2009)
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