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NAACL
2003

Weakly Supervised Natural Language Learning Without Redundant Views

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Weakly Supervised Natural Language Learning Without Redundant Views
We investigate single-view algorithms as an alternative to multi-view algorithms for weakly supervised learning for natural language processing tasks without a natural feature split. In particular, we apply co-training, self-training, and EM to one such task and find that both selftraining and FS-EM, a new variation of EM that incorporates feature selection, outperform cotraining and are comparatively less sensitive to parameter changes.
Vincent Ng, Claire Cardie
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NAACL
Authors Vincent Ng, Claire Cardie
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