@inproceedings{lin-etal-2017-multi,
    title = "Multi-channel {B}i{LSTM}-{CRF} Model for Emerging Named Entity Recognition in Social Media",
    author = "Lin, Bill Y.  and
      Xu, Frank  and
      Luo, Zhiyi  and
      Zhu, Kenny",
    editor = "Derczynski, Leon  and
      Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W17-4421/",
    doi = "10.18653/v1/W17-4421",
    pages = "160--165",
    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."
}