A Kernel Canonical Correlation Analysis for Learning the Semantics of Text
2011 ◽
pp. 263-282
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Keyword(s):
We present a general method using kernel canonical correlation analysis (KCCA) to learn a semantic of text from an aligned multilingual collection of text documents. The semantic space provides a language-independent representation of text and enables a comparison between the text documents from different languages. In experiments, we apply the KCCA to the cross-lingual retrieval of text documents, where the text query is written in only one language, and to cross-lingual text categorization, where we trained a cross-lingual classifier.
2004 ◽
Vol 16
(12)
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pp. 2639-2664
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2012 ◽
Vol 437
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pp. 1-13
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2011 ◽
Vol 5
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pp. 2169-2196
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2019 ◽
Vol 17
(04)
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pp. 1950028
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2011 ◽
Vol 32
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pp. 1572-1583
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2019 ◽
Vol 58
(39)
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pp. 18280-18291
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