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1
Learning verb classes in an incremental model
In: http://anthology.aclweb.org/W/W14/W14-2005.pdf (2014)
Abstract: The ability of children to generalize over the linguistic input they receive is key to acquiring productive knowledge of verbs. Such generalizations help children extend their learned knowledge of constructions to a novel verb, and use it appropriately in syntactic patterns previously unobserved for that verb—a key factor in language productivity. Computational models can help shed light on the gradual development of more abstract knowledge during verb acquisition. We present an incremental Bayesian model that simultaneously and incrementally learns argument structure constructions and verb classes given nat-uralistic language input. We show how the distributional properties in the input lan-guage influence the formation of general-izations over the constructions and classes. 1
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.672.5127
http://anthology.aclweb.org/W/W14/W14-2005.pdf
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2
A probabilistic computational model of cross-situational word learning
In: http://www.coli.uni-saarland.de/~afra/papers/Fazlyetal_CS2009.pdf (2010)
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3
A probabilistic computational model of cross-situational word learning
In: http://www.cs.toronto.edu/~afsaneh/FazlyEtAl-CSJ10.pdf (2010)
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4
Cross-situational learning of low frequency words: The role of context familiarity and age of exposure
In: http://www.cs.toronto.edu/~suzanne/papers/FazlyEtAl-CogSci2010.pdf (2010)
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5
Cross-situational learning of low frequency words: The role of context familiarity and age of exposure
In: http://www.cs.toronto.edu/%7Eafsaneh/FazlyEtAl-CogSci11.pdf (2010)
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6
Unsupervised Type and Token Identification of Idiomatic Expression
In: http://aclweb.org/anthology-new/J/J09/J09-1005.pdf (2009)
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7
Unsupervised type and token identification of idiomatic expressions
In: http://www.cs.toronto.edu/%7Esuzanne/papers/FazlyEtAlToAppear.pdf (2009)
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8
A probabilistic incremental model of word learning in the presence of referential uncertainty
In: http://csjarchive.cogsci.rpi.edu/Proceedings/2008/pdfs/p703.pdf (2008)
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9
An incremental bayesian model for learning syntactic categories
In: http://www.cs.toronto.edu/~afsaneh/ParisienEtAl-CoNLL08.pdf (2008)
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10
The VNCTokens Dataset
In: http://www.cs.toronto.edu/~pcook/CFS2008.pdf (2008)
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11
The VNCTokens Dataset
In: http://www.cs.toronto.edu/~suzanne/papers/CFS2008.pdf (2008)
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12
Distinguishing subtypes of multiword expressions using linguistically-motivated statistical measures
In: http://www.aclweb.org/anthology-new/W/W07/W07-1102.pdf (2007)
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13
Distinguishing subtypes of multiword expressions using linguistically-motivated statistical measures. ACL2007 Workshop on Multiword Expressions: A Broader Perspective on Multiword Expressions
In: http://www.let.uu.nl/~nicole.gregoire/personal/acl07-mwe/pdf/acl07-mwe02.pdf (2007)
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14
Distinguishing subtypes of multiword expressions using linguistically-motivated statistical measures. ACL2007 Workshop on Multiword Expressions: A Broader Perspective on Multiword Expressions
In: http://www.cs.toronto.edu/~suzanne/papers/FazlyStevenson2007.pdf (2007)
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15
Automatically determining allowable combinations of a class of flexible multiword expressions
In: http://www.cs.toronto.edu/~suzanne/papers/FazlyEtAl2006.pdf (2006)
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16
Automatically determining allowable combinations of a class of flexible multiword expressions
In: http://www.cs.toronto.edu/~afsaneh/FazlyEtAl2006.pdf (2006)
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17
Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition, pages 38--47
In: http://acl.ldc.upenn.edu/W/W05/W05-1005.pdf (2005)
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18
Automatically distinguishing literal and figurative usages of highly polysemous verbs
In: http://www.cs.toronto.edu/~suzanne/papers/wdla05-ref.pdf (2005)
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19
Automatically distinguishing literal and figurative usages of highly polysemous verbs
In: http://ftp.cs.toronto.edu/pub/gh/Fazly-etal-2005.pdf (2005)
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20
Statistical measures of the semi-productivity of light verb constructions
In: http://acl.ldc.upenn.edu/acl2004/mwe/pdf/stevenson.pdf (2004)
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