@inproceedings{sui-etal-2019-leverage,
title = "Leverage Lexical Knowledge for {C}hinese Named Entity Recognition via Collaborative Graph Network",
author = "Sui, Dianbo and
Chen, Yubo and
Liu, Kang and
Zhao, Jun and
Liu, Shengping",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1396",
doi = "10.18653/v1/D19-1396",
pages = "3830--3840",
abstract = "The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system. Fortunately, the automatically constructed lexicon contains rich word boundaries information and word semantic information. However, integrating lexical knowledge in Chinese NER tasks still faces challenges when it comes to self-matched lexical words as well as the nearest contextual lexical words. We present a Collaborative Graph Network to solve these challenges. Experiments on various datasets show that our model not only outperforms the state-of-the-art (SOTA) results, but also achieves a speed that is six to fifteen times faster than that of the SOTA model.",
}
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<abstract>The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system. Fortunately, the automatically constructed lexicon contains rich word boundaries information and word semantic information. However, integrating lexical knowledge in Chinese NER tasks still faces challenges when it comes to self-matched lexical words as well as the nearest contextual lexical words. We present a Collaborative Graph Network to solve these challenges. Experiments on various datasets show that our model not only outperforms the state-of-the-art (SOTA) results, but also achieves a speed that is six to fifteen times faster than that of the SOTA model.</abstract>
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%0 Conference Proceedings
%T Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network
%A Sui, Dianbo
%A Chen, Yubo
%A Liu, Kang
%A Zhao, Jun
%A Liu, Shengping
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F sui-etal-2019-leverage
%X The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system. Fortunately, the automatically constructed lexicon contains rich word boundaries information and word semantic information. However, integrating lexical knowledge in Chinese NER tasks still faces challenges when it comes to self-matched lexical words as well as the nearest contextual lexical words. We present a Collaborative Graph Network to solve these challenges. Experiments on various datasets show that our model not only outperforms the state-of-the-art (SOTA) results, but also achieves a speed that is six to fifteen times faster than that of the SOTA model.
%R 10.18653/v1/D19-1396
%U https://aclanthology.org/D19-1396
%U https://doi.org/10.18653/v1/D19-1396
%P 3830-3840
Markdown (Informal)
[Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network](https://aclanthology.org/D19-1396) (Sui et al., EMNLP-IJCNLP 2019)
ACL