Active Learning with Partially Annotated Sequence (自然言語処理(NL) : 学習・応用) Active Learning with Partially Annotated Sequence

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Abstract

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.

Journal

  • 研究報告自然言語処理(NL)

    研究報告自然言語処理(NL) 2010-NL-198(4), 1-7, 2010-09-09

    情報処理学会

References:  20

Codes

  • NII Article ID (NAID)
    110008003297
  • NII NACSIS-CAT ID (NCID)
    AN10115061
  • Text Lang
    ENG
  • Article Type
    Technical Report
  • ISSN
    09196072
  • NDL Article ID
    025244523
  • NDL Call No.
    YH247-911
  • Data Source
    CJP  NDL  NII-ELS  IPSJ 
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