Active Learning with Partially Annotated Sequence
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概要
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We propose an active learning framework which requires human annotation only in the ambiguous parts of the sequence. In each iteration of active learning, a set of tokens from the ambiguous parts are manually labeled while the other tokens are left unannotated. Our proposed method is superior to the method where unambiguous tokens are automatically labeled. We evaluate our proposed framework on chunking and named entity recognition data provided by CoNLL. Experimental results show that our proposed framework outperforms the previous work using automatically labeled tokens, and almost reaches the supervised F1 with 6.37% and 8.59% of tokens being manually labeled, respectively.
- 2010-09-09
著者
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Dittaya Wanvarie
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology
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Hiroya Takamura
Precision and Intelligence Laboratory, Tokyo Institute of Technology
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Manabu Okumura
Precision and Intelligence Laboratory, Tokyo Institute of Technology
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Manabu Okumura
Precision And Intelligence Laboratory Tokyo Institute Of Technology
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Hiroya Takamura
Precision And Intelligence Laboratory Tokyo Institute Of Technology
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Dittaya Wanvarie
Department Of Computational Intelligence And Systems Science Tokyo Institute Of Technology
関連論文
- Active Learning with Partially Annotated Sequence
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