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- W1977896902 abstract "We present two semi-supervised learning techniques to improve a state-of-the-art multi-lingual name tagger. For English and Chinese, the overall system obtains 1.7% - 2.1% improvement in F-measure, representing a 13.5% -- 17.4% relative reduction in the spurious, missing, and incorrect tags. We also conclude that simply relying upon large corpora is not in itself sufficient: we must pay attention to unlabeled data selection too. We describe effective measures to automatically select documents and sentences." @default.
- W1977896902 created "2016-06-24" @default.
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- W1977896902 date "2006-01-01" @default.
- W1977896902 modified "2023-10-03" @default.
- W1977896902 title "Data selection in semi-supervised learning for name tagging" @default.
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- W1977896902 doi "https://doi.org/10.3115/1641408.1641414" @default.
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