2026-05-12T07:14:52Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00070312 2025-01-21T23:30:31Z 1164:4179:6002:6162

Active Learning with Partially Annotated Sequence Active Learning with Partially Annotated Sequence Dittaya, Wanvarie Hiroya, Takamura Manabu, Okumura Dittaya, Wanvarie Hiroya, Takamura Manabu, Okumura 学習・応用 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. 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. technical report 情報処理学会 2010-09-09 application/pdf 研究報告自然言語処理（NL） 4 2010-NL-198 1 7 AN10115061 https://ipsj.ixsq.nii.ac.jp/record/70312/files/IPSJ-NL10198004.pdf eng