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A Fast Implementation of Rules Based Machine Translation Systems for Similar Natural Languages
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In: http://www.informatica.si/PDF/37-4/31_Vicic - A Fast Implementation of Rules Based Machine Translation Systems for Similar Natural Languages.pdf (2013)
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L1 transfer in article selection for generic reference by Spanish, Turkish and Japanese L2 learners
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In: http://revistas.um.es/ijes/article/download/138701/147991/ (2011)
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Networks uncover hidden lexical borrowing in Indo-European language evolution
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In: ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/17/32/Proc_Biol_Sci_2011_Jun_22_278(1713)_1794-1803.tar.gz (2010)
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Effect of Linguistic Heterogeneity on Technology Transfer: An Economic Study of FIFA Football Rankings.
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In: http://mpra.ub.uni-muenchen.de/27181/1/submanu_101112.pdf (2010)
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Knowledge transformation for cross-domain sentiment classification
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In: http://users.cis.fiu.edu/~taoli/pub/sigir09-p716-li.pdf (2009)
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Loan Words Get By with Little Help from Do Non-native Entities and Structures
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In: http://www.cl.uni-heidelberg.de/~holler/bamberg/DGfS_Handout_Nakajima.pdf (2008)
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Effect of Linguistic Heterogeneity on Technology Transfer: An Economic Study of FIFA Football Rankings.
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In: http://mpra.ub.uni-muenchen.de/10305/1/MPRA_paper_10305.pdf (2008)
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EVect of
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In: http://www.hebpsy.net/bf/P3cOms4ZJPOtjjUHgI4o.pdf (2004)
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World Scientific Publishing Company Machine Translation of English Noun Phrases into Arabic
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In: http://mti.ugm.ac.id/~adji/courses/resources/doctor/WorldSci/2004/Jun/MTenglish-arabNoun04.pdf (2004)
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Experiments with a Hindi-toEnglish transfer-based MT system under a miserly data scenario
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In: http://www.mt-archive.info/TALIP-2003-Lavie.pdf (2003)
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UNL Lexical Selection with Conceptual Vectors
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In: http://www.lrec-conf.org/proceedings/lrec2002/pdf/354.pdf (2002)
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Transfer effects of repeated EFL reading on reading new passages: A Preliminary investigation. Reading in a Foreign Language
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In: http://nflrc.hawaii.edu/rfl/April2002/taguchi/taguchi.pdf (2002)
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Resource Alignment and Implicit Transfer
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In: http://www.mt-archive.info/MTS-2001-Senellart-2.pdf (2001)
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Rapid assembly of a large-scale French-English MT system
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In: http://research.microsoft.com/nlp/publications/mtsummit01-fesystem-final.pdf (2001)
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On UNL as the future html of the linguistic content and the reuse of existing NLP components in UNL-related applications with the example of a UNL-French deconverter
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In: http://www-clips.imag.fr/geta/gilles.serasset/GSCB-COLING2000.pdf (2000)
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On UNL as the future "html of the linguistic content" & the reuse of existing NLP components in UNL-related applications with the example of a UNL-French deconverter
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In: http://acl.ldc.upenn.edu/C/C00/C00-2111.pdf (2000)
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On UNL as the future "html of the linguistic content" the reuse of existing NLP components in UNL-related applications with the example of a UNL-French deconverter
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In: http://nlp3.korea.ac.kr/proceeding/coling2000/COLING/ps/111.ps (2000)
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From Robotic Toil to Symbolc Theft: Grounding Transfer from Entry-Level to Higher-Level Categories
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In: http://www.tech.plym.ac.uk/soc/staff/angelo/papers/Cangelosi-ConnScie2000.pdf (2000)
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Abstract:
Neural network models of categorical perception (compression of within-category similarity and dilation of between-category differences) are applied to the symbol-grounding problem (of how to connect symbols with meanings) by connecting analog sensorimotor projections to arbitrary symbolic representations via learned category-invariance detectors in a hybrid symbolic/nonsymbolic system. Our nets are trained to categorize and name 50x50 pixel images (e.g., circles, ellipses, squares and rectangles) projected onto the receptive field of a 7x7 retina. They first learn to do prototype matching and then entry-level naming for the four kinds of stimuli, grounding their names directly in the input patterns via hidden-unit representations ("sensorimotor toil"). We show that a higher-level categorization (e.g., "symmetric" vs. "asymmetric") can learned in two very different ways: either (1) directly from the input, just as with the entry-level categories (i.e., by toil), or (2) indirectly, from boolean combinations of the grounded category names in the form of propositions describing the higher-order category ("symbolic theft"). We analyze the architectures and input conditions that allow grounding (in the form of compression/separation in internal similarity space) to be "transferred" in this second way from directly grounded entry-level category names to higher-order category names. Such hybrid models have implications for the evolution and learning of language.
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Keyword:
From Robotic Toil to Symbolic Theft; Grounding Transfer from
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.9857 http://www.tech.plym.ac.uk/soc/staff/angelo/papers/Cangelosi-ConnScie2000.pdf
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Extendible Structural Transfer: A Case For German To Turkish Translation
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In: ftp://ftp.lcsl.metu.edu.tr/pub/theses/kopru-ms-99.ps.gz (1999)
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Structural Transfer In An English To Turkish Machine Translation System
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In: ftp://ftp.lcsl.metu.edu.tr/pub/theses/keyderturhan-phd-98.ps.gz (1998)
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