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    <abstract>In this paper, we present our multi-channel neural architecture for recognizing emerging named entity in social media messages, which we applied in the Novel and Emerging Named Entity Recognition shared task at the EMNLP 2017 Workshop on Noisy User-generated Text (W-NUT). We propose a novel approach, which incorporates comprehensive word representations with multi-channel information and Conditional Random Fields (CRF) into a traditional Bidirectional Long Short-Term Memory (BiLSTM) neural network without using any additional hand-craft features such as gazetteers. In comparison with other systems participating in the shared task, our system won the 2nd place.</abstract>
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%0 Conference Proceedings
%T Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media
%A Lin, Bill Y.
%A Xu, Frank
%A Luo, Zhiyi
%A Zhu, Kenny
%Y Derczynski, Leon
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%S Proceedings of the 3rd Workshop on Noisy User-generated Text
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F lin-etal-2017-multi
%X In this paper, we present our multi-channel neural architecture for recognizing emerging named entity in social media messages, which we applied in the Novel and Emerging Named Entity Recognition shared task at the EMNLP 2017 Workshop on Noisy User-generated Text (W-NUT). We propose a novel approach, which incorporates comprehensive word representations with multi-channel information and Conditional Random Fields (CRF) into a traditional Bidirectional Long Short-Term Memory (BiLSTM) neural network without using any additional hand-craft features such as gazetteers. In comparison with other systems participating in the shared task, our system won the 2nd place.
%R 10.18653/v1/W17-4421
%U https://aclanthology.org/W17-4421
%U https://doi.org/10.18653/v1/W17-4421
%P 160-165

Markdown (Informal)

[Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media](https://aclanthology.org/W17-4421) (Lin et al., WNUT 2017)

• Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media (Lin et al., WNUT 2017)

ACL

• Bill Y. Lin, Frank Xu, Zhiyi Luo, and Kenny Zhu. 2017. Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media. In Proceedings of the 3rd Workshop on Noisy User-generated Text, pages 160–165, Copenhagen, Denmark. Association for Computational Linguistics.