Word-Context Character Embeddings for Chinese Word Segmentation

Hao Zhou (昊 周); Zhenting Yu; Yue Zhang (张岳, 章岳); Shujian Huang (书剑 黄); Xinyu Dai; Jiajun Chen

doi:10.18653/v1/D17-1079

Word-Context Character Embeddings for Chinese Word Segmentation

Hao Zhou, Zhenting Yu, Yue Zhang, Shujian Huang, Xinyu Dai, Jiajun Chen

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Abstract

Neural parsers have benefited from automatically labeled data via dependency-context word embeddings. We investigate training character embeddings on a word-based context in a similar way, showing that the simple method improves state-of-the-art neural word segmentation models significantly, beating tri-training baselines for leveraging auto-segmented data.

Anthology ID:: D17-1079
Volume:: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:: September
Year:: 2017
Address:: Copenhagen, Denmark
Editors:: Martha Palmer, Rebecca Hwa, Sebastian Riedel
Venue:: EMNLP
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 760–766
Language:
URL:: https://aclanthology.org/D17-1079/
DOI:: 10.18653/v1/D17-1079
Bibkey:
Cite (ACL):: Hao Zhou, Zhenting Yu, Yue Zhang, Shujian Huang, Xinyu Dai, and Jiajun Chen. 2017. Word-Context Character Embeddings for Chinese Word Segmentation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 760–766, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):: Word-Context Character Embeddings for Chinese Word Segmentation (Zhou et al., EMNLP 2017)
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PDF:: https://aclanthology.org/D17-1079.pdf

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@inproceedings{zhou-etal-2017-word,
    title = "Word-Context Character Embeddings for {C}hinese Word Segmentation",
    author = "Zhou, Hao  and
      Yu, Zhenting  and
      Zhang, Yue  and
      Huang, Shujian  and
      Dai, Xinyu  and
      Chen, Jiajun",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D17-1079/",
    doi = "10.18653/v1/D17-1079",
    pages = "760--766",
    abstract = "Neural parsers have benefited from automatically labeled data via dependency-context word embeddings. We investigate training character embeddings on a word-based context in a similar way, showing that the simple method improves state-of-the-art neural word segmentation models significantly, beating tri-training baselines for leveraging auto-segmented data."
}

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%0 Conference Proceedings
%T Word-Context Character Embeddings for Chinese Word Segmentation
%A Zhou, Hao
%A Yu, Zhenting
%A Zhang, Yue
%A Huang, Shujian
%A Dai, Xinyu
%A Chen, Jiajun
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F zhou-etal-2017-word
%X Neural parsers have benefited from automatically labeled data via dependency-context word embeddings. We investigate training character embeddings on a word-based context in a similar way, showing that the simple method improves state-of-the-art neural word segmentation models significantly, beating tri-training baselines for leveraging auto-segmented data.
%R 10.18653/v1/D17-1079
%U https://aclanthology.org/D17-1079/
%U https://doi.org/10.18653/v1/D17-1079
%P 760-766

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Markdown (Informal)

[Word-Context Character Embeddings for Chinese Word Segmentation](https://aclanthology.org/D17-1079/) (Zhou et al., EMNLP 2017)

Word-Context Character Embeddings for Chinese Word Segmentation (Zhou et al., EMNLP 2017)

ACL

Hao Zhou, Zhenting Yu, Yue Zhang, Shujian Huang, Xinyu Dai, and Jiajun Chen. 2017. Word-Context Character Embeddings for Chinese Word Segmentation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 760–766, Copenhagen, Denmark. Association for Computational Linguistics.