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Simulating the referential properties of Dutch, German and English Root Infinitives in MOSAIC
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104 |
Linking working memory and long-term memory: A computational model of the learning of new words
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Jones, G; Gobet, F; Pine, J M. - : Blackwell Publishing. The definitive version is available at onlinelibrary.wiley.com, 2007
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105 |
24.906J / 21F.024J The Linguistic Study of Bilingualism, Fall 2006 ; The Linguistic Study of Bilingualism
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106 |
Does repetition improve word retrieval in speakers with aphasia?
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107 |
The Tense-Aspect system between ontogeny and phylogeny: Evidence from the Proto-Indo-European “Injunctive”
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108 |
24.900 Introduction to Linguistics, Spring 2005 ; Introduction to Linguistics
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109 |
Agenesis of the corpus callosum: A case study [abstract]
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In: http://undergradresearch.missouri.edu/forums-conferences/abstracts/abstract-detail.php?abstractid=379 (2005)
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110 |
Resolving ambiguities in the extraction of syntactic categories through chunking.
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111 |
The Sound Barrier: Two-Year-Old Children's Use of Newly Acquired Words to Describe Preverbal Memories
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112 |
24.900 Introduction to Linguistics, Spring 2004 ; Introduction to Linguistics
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113 |
A Corpus Study on the Item-based Nature of Early Grammar Acquisition ...
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115 |
How to do things without words : infants, utterance-activity and distributed cognition
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116 |
A Computational Model of Embodied Language Learning
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Abstract:
Language is about symbols and those symbols must be grounded in the physical environment during human development. Most recently, there has been an increased awareness of the essential role of inferences of speakersU referential intentions in grounding those symbols. Experiments have shown that these inferences as revealed in eye, head and hand movements serve as an important driving force in language learning at a relatively early age. The challenge ahead is to develop formal models of language acquisition that can shed light on the leverage provided by embodiment. We present an implemented computational model of embodied language acquisition that learns words from natural interactions with users. The system can be trained in unsupervised mode in which users perform everyday tasks while providing natural language descriptions of their behaviors. We collect acoustic signals in concert with user-centric multisensory information from nonspeech modalities, such as userUs perspective video, gaze positions, head directions and hand movements. A multimodal learning algorithm is developed that firstly spots words from continuous speech and then associates action verbs and object names with their grounded meanings. The central idea is to make use of non-speech contextual information to facilitate word spotting, and utilize userUs attention as deictic reference to discover temporal correlations of data from different modalities to build lexical items. We report the results of a series of experiments that demonstrate the effectiveness of our approach.
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Keyword:
cognitive modeling; integration of speech and vision; language acquisition; multimodal learning
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URL: http://hdl.handle.net/1802/725
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117 |
Modelling children's negation errors using probabilistic learning in MOSAIC.
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118 |
Learning Grammatical Constructions in a Miniature anguage from Narrated Video Events
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In: https://hal.archives-ouvertes.fr/hal-00089640 ; 2003 (2003)
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119 |
Modelling the development of Dutch Optional Infinitives in MOSAIC.
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