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        <title>CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition</title>
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    <name type="personal">
        <namePart type="given">Yuying</namePart>
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    <name type="personal">
        <namePart type="given">Guoxin</namePart>
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        <dateIssued>2019-06</dateIssued>
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            <title>Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Jill</namePart>
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    <abstract>Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as the first step for Chinese NER. However, models based on word-level embeddings and lexicon features often suffer from segmentation errors and out-of-vocabulary (OOV) words. In this paper, we investigate a Convolutional Attention Network called CAN for Chinese NER, which consists of a character-based convolutional neural network (CNN) with local-attention layer and a gated recurrent unit (GRU) with global self-attention layer to capture the information from adjacent characters and sentence contexts. Also, compared to other models, not depending on any external resources like lexicons and employing small size of char embeddings make our model more practical. Extensive experimental results show that our approach outperforms state-of-the-art methods without word embedding and external lexicon resources on different domain datasets including Weibo, MSRA and Chinese Resume NER dataset.</abstract>
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    <identifier type="doi">10.18653/v1/N19-1342</identifier>
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        <date>2019-06</date>
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%0 Conference Proceedings
%T CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
%A Zhu, Yuying
%A Wang, Guoxin
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F zhu-wang-2019-ner
%X Named entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as the first step for Chinese NER. However, models based on word-level embeddings and lexicon features often suffer from segmentation errors and out-of-vocabulary (OOV) words. In this paper, we investigate a Convolutional Attention Network called CAN for Chinese NER, which consists of a character-based convolutional neural network (CNN) with local-attention layer and a gated recurrent unit (GRU) with global self-attention layer to capture the information from adjacent characters and sentence contexts. Also, compared to other models, not depending on any external resources like lexicons and employing small size of char embeddings make our model more practical. Extensive experimental results show that our approach outperforms state-of-the-art methods without word embedding and external lexicon resources on different domain datasets including Weibo, MSRA and Chinese Resume NER dataset.
%R 10.18653/v1/N19-1342
%U https://aclanthology.org/N19-1342
%U https://doi.org/10.18653/v1/N19-1342
%P 3384-3393

Markdown (Informal)

[CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition](https://aclanthology.org/N19-1342) (Zhu & Wang, NAACL 2019)

• CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition (Zhu & Wang, NAACL 2019)

ACL

• Yuying Zhu and Guoxin Wang. 2019. CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3384–3393, Minneapolis, Minnesota. Association for Computational Linguistics.