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    <titleInfo>
        <title>Segment-Level Neural Conditional Random Fields for Named Entity Recognition</title>
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    <name type="personal">
        <namePart type="given">Motoki</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Ikuya</namePart>
        <namePart type="family">Yamada</namePart>
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    <name type="personal">
        <namePart type="given">Yuji</namePart>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)</title>
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            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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        <name type="personal">
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    <abstract>We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking. Our segment-level CRF can consider higher-order label dependencies compared with conventional word-level CRF. Since it is difficult to consider all possible variable length segments, our method uses segment lattice constructed from the word-level tagging model to reduce the search space. Performing experiments on NER and chunking, we demonstrate that our method outperforms conventional word-level CRF with neural networks.</abstract>
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%0 Conference Proceedings
%T Segment-Level Neural Conditional Random Fields for Named Entity Recognition
%A Sato, Motoki
%A Shindo, Hiroyuki
%A Yamada, Ikuya
%A Matsumoto, Yuji
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F sato-etal-2017-segment
%X We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking. Our segment-level CRF can consider higher-order label dependencies compared with conventional word-level CRF. Since it is difficult to consider all possible variable length segments, our method uses segment lattice constructed from the word-level tagging model to reduce the search space. Performing experiments on NER and chunking, we demonstrate that our method outperforms conventional word-level CRF with neural networks.
%U https://aclanthology.org/I17-2017
%P 97-102

Markdown (Informal)

[Segment-Level Neural Conditional Random Fields for Named Entity Recognition](https://aclanthology.org/I17-2017) (Sato et al., IJCNLP 2017)

• Segment-Level Neural Conditional Random Fields for Named Entity Recognition (Sato et al., IJCNLP 2017)

ACL

• Motoki Sato, Hiroyuki Shindo, Ikuya Yamada, and Yuji Matsumoto. 2017. Segment-Level Neural Conditional Random Fields for Named Entity Recognition. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 97–102, Taipei, Taiwan. Asian Federation of Natural Language Processing.