@inproceedings{cai-etal-2017-fast,
title = "Fast and Accurate Neural Word Segmentation for {C}hinese",
author = "Cai, Deng and
Zhao, Hai and
Zhang, Zhisong and
Xin, Yuan and
Wu, Yongjian and
Huang, Feiyue",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2096",
doi = "10.18653/v1/P17-2096",
pages = "608--615",
abstract = "Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation. However, both training and working procedures of the current neural models are computationally inefficient. In this paper, we propose a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of performing segmentation much faster and even more accurate than state-of-the-art neural models on Chinese benchmark datasets.",
}
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%0 Conference Proceedings
%T Fast and Accurate Neural Word Segmentation for Chinese
%A Cai, Deng
%A Zhao, Hai
%A Zhang, Zhisong
%A Xin, Yuan
%A Wu, Yongjian
%A Huang, Feiyue
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F cai-etal-2017-fast
%X Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation. However, both training and working procedures of the current neural models are computationally inefficient. In this paper, we propose a greedy neural word segmenter with balanced word and character embedding inputs to alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of performing segmentation much faster and even more accurate than state-of-the-art neural models on Chinese benchmark datasets.
%R 10.18653/v1/P17-2096
%U https://aclanthology.org/P17-2096
%U https://doi.org/10.18653/v1/P17-2096
%P 608-615
Markdown (Informal)
[Fast and Accurate Neural Word Segmentation for Chinese](https://aclanthology.org/P17-2096) (Cai et al., ACL 2017)
ACL
- Deng Cai, Hai Zhao, Zhisong Zhang, Yuan Xin, Yongjian Wu, and Feiyue Huang. 2017. Fast and Accurate Neural Word Segmentation for Chinese. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 608–615, Vancouver, Canada. Association for Computational Linguistics.