Chinese Word Segmentation Based on Conditional Random Field

Junxia Deng; Hong Zhang; Shanzai Li

doi:doi:10.11648/j.mlr.20170203.14

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Chinese Word Segmentation Based on Conditional Random Field

Junxia Deng, Hong Zhang, Shanzai Li

Published in Machine Learning Research (Volume 2, Issue 3)

Received: 6 February 2017 Accepted: 27 February 2017 Published: 17 April 2017

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Abstract

This paper systematically describes the definition, model structure, parameter estimation and corpus selection of the conditional random field model, and applies the conditional random field to the Chinese word segmentation and the Chinese word segmentation method. In this paper, a large number of experiments have been carried out using conditional random fields. The experimental corpus has been tested by Changjiang Daily for many years. Experiments are carried out to analyze the influence of the choice of conditional random field model parameters and the selection of Chinese character annotation sets on the experimental results. Furthermore, the condition of random field model can be used to add the advantages of arbitrary features, and some new features are added to the model. Word probability, the paper explores the probability characteristic of word location. Experiments on the corpus show that the introduction of the word position probability feature has improved the accuracy, recall and the value of Fl.

Published in	Machine Learning Research (Volume 2, Issue 3)
DOI	10.11648/j.mlr.20170203.14
Page(s)	105-109
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Natural Language Processing, Chinese Word Segmentation, Hidden Markov Model, Maximum Entropy Model, Conditional Random Field, Automatic Proofreading

References

[1]	John Lafferty, Andrew McCallum, F Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the 18th international Conference on Machine Leaning. San Francisco, USA. 2001: 282-289.
[2]	Lafferty J, McCallum A, Pereira F. Conditional random fields: probabilistic models for segmenting and labeling sequence data [A]. Processing of the International Conference on Machine Learning (ICML-2001) [C]. Williams college, MA, 2001: 282-289.
[3]	Pinto D, McCallum A, Wei X et al. Table extraction using conditional random fields [A]. Proceedings of the 26th ACM SIGm [C], Toronto, Canada, 2003: 235-242.
[4]	David Palmer A Trainable Rule-based Algorithm for Word Segmentation 1997.
[5]	Berkeley, California, A new statistical formula for Chinese text segmentation incorporating contextual information. United States Pages: 82-89 Year of Publication: 1999.
[6]	Lawrence R. Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition proceedings of The IEEE 77 (2): pp. ZS7-286, 1989.
[7]	Zhou, GD., Su J. Named entity Recognition using all HMM-based chunk tagger. 2002.
[8]	E. T. Jaynes. information Theory and Statistica Imeehanics. 1957.
[9]	J. R. Crran and S. Clark Investigatigating GIS and Smoothing for Maximum Entropy Tggers. Proceedings of the llh Conference of the Europe Chapter of the Association of Computation Linguistics (EACL), Pages 91—98, Budapest, Hungary. 2003.
[10]	Tan Y’Yao T, Chea Q ET al. Applying conditional random fields to Chinese shallow parsing
[11]	Proceedings of CICLing-2005 [c], Mexico City, Mexico, 2005: 167-176.
[12]	Kudo T, Yamamoto K, Matsumoto Y. Applying Conditional Random Fields to Japanese Morphological Analysis [A]. Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP-2004) [C], Barcelona, 2004: 230-237.
[13]	Zhou J, Dai X, Ni R et al. A hybrid approach to Chinese word segmentation around CRFs [A]. Proceedings of the Fourth SIGHAN Workshop on Chinese language Processing [C], Jeju Island, Korea, 2005: 196-199.

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APA Style

Junxia Deng, Hong Zhang, Shanzai Li. (2017). Chinese Word Segmentation Based on Conditional Random Field. Machine Learning Research, 2(3), 105-109. https://doi.org/10.11648/j.mlr.20170203.14

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ACS Style

Junxia Deng; Hong Zhang; Shanzai Li. Chinese Word Segmentation Based on Conditional Random Field. Mach. Learn. Res. 2017, 2(3), 105-109. doi: 10.11648/j.mlr.20170203.14

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AMA Style

Junxia Deng, Hong Zhang, Shanzai Li. Chinese Word Segmentation Based on Conditional Random Field. Mach Learn Res. 2017;2(3):105-109. doi: 10.11648/j.mlr.20170203.14

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@article{10.11648/j.mlr.20170203.14,
  author = {Junxia Deng and Hong Zhang and Shanzai Li},
  title = {Chinese Word Segmentation Based on Conditional Random Field},
  journal = {Machine Learning Research},
  volume = {2},
  number = {3},
  pages = {105-109},
  doi = {10.11648/j.mlr.20170203.14},
  url = {https://doi.org/10.11648/j.mlr.20170203.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170203.14},
  abstract = {This paper systematically describes the definition, model structure, parameter estimation and corpus selection of the conditional random field model, and applies the conditional random field to the Chinese word segmentation and the Chinese word segmentation method. In this paper, a large number of experiments have been carried out using conditional random fields. The experimental corpus has been tested by Changjiang Daily for many years. Experiments are carried out to analyze the influence of the choice of conditional random field model parameters and the selection of Chinese character annotation sets on the experimental results. Furthermore, the condition of random field model can be used to add the advantages of arbitrary features, and some new features are added to the model. Word probability, the paper explores the probability characteristic of word location. Experiments on the corpus show that the introduction of the word position probability feature has improved the accuracy, recall and the value of Fl.},
 year = {2017}
}

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TY  - JOUR
T1  - Chinese Word Segmentation Based on Conditional Random Field
AU  - Junxia Deng
AU  - Hong Zhang
AU  - Shanzai Li
Y1  - 2017/04/17
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20170203.14
DO  - 10.11648/j.mlr.20170203.14
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 105
EP  - 109
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20170203.14
AB  - This paper systematically describes the definition, model structure, parameter estimation and corpus selection of the conditional random field model, and applies the conditional random field to the Chinese word segmentation and the Chinese word segmentation method. In this paper, a large number of experiments have been carried out using conditional random fields. The experimental corpus has been tested by Changjiang Daily for many years. Experiments are carried out to analyze the influence of the choice of conditional random field model parameters and the selection of Chinese character annotation sets on the experimental results. Furthermore, the condition of random field model can be used to add the advantages of arbitrary features, and some new features are added to the model. Word probability, the paper explores the probability characteristic of word location. Experiments on the corpus show that the introduction of the word position probability feature has improved the accuracy, recall and the value of Fl.
VL  - 2
IS  - 3
ER  -

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Author Information

Junxia Deng

International Economics and Trade, Gengdan Institute of Beijing University of Technology, Beijing, China
Hong Zhang

School of Information, Beijing Wuzi University, Beijing, China
Shanzai Li

School of Information, Beijing Wuzi University, Beijing, China

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Plain Text BibTeX RIS

APA Style

Junxia Deng, Hong Zhang, Shanzai Li. (2017). Chinese Word Segmentation Based on Conditional Random Field. Machine Learning Research, 2(3), 105-109. https://doi.org/10.11648/j.mlr.20170203.14

Copy | Download

ACS Style

Junxia Deng; Hong Zhang; Shanzai Li. Chinese Word Segmentation Based on Conditional Random Field. Mach. Learn. Res. 2017, 2(3), 105-109. doi: 10.11648/j.mlr.20170203.14

Copy | Download

AMA Style

Junxia Deng, Hong Zhang, Shanzai Li. Chinese Word Segmentation Based on Conditional Random Field. Mach Learn Res. 2017;2(3):105-109. doi: 10.11648/j.mlr.20170203.14

Copy | Download

@article{10.11648/j.mlr.20170203.14,
  author = {Junxia Deng and Hong Zhang and Shanzai Li},
  title = {Chinese Word Segmentation Based on Conditional Random Field},
  journal = {Machine Learning Research},
  volume = {2},
  number = {3},
  pages = {105-109},
  doi = {10.11648/j.mlr.20170203.14},
  url = {https://doi.org/10.11648/j.mlr.20170203.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170203.14},
  abstract = {This paper systematically describes the definition, model structure, parameter estimation and corpus selection of the conditional random field model, and applies the conditional random field to the Chinese word segmentation and the Chinese word segmentation method. In this paper, a large number of experiments have been carried out using conditional random fields. The experimental corpus has been tested by Changjiang Daily for many years. Experiments are carried out to analyze the influence of the choice of conditional random field model parameters and the selection of Chinese character annotation sets on the experimental results. Furthermore, the condition of random field model can be used to add the advantages of arbitrary features, and some new features are added to the model. Word probability, the paper explores the probability characteristic of word location. Experiments on the corpus show that the introduction of the word position probability feature has improved the accuracy, recall and the value of Fl.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Chinese Word Segmentation Based on Conditional Random Field
AU  - Junxia Deng
AU  - Hong Zhang
AU  - Shanzai Li
Y1  - 2017/04/17
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20170203.14
DO  - 10.11648/j.mlr.20170203.14
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 105
EP  - 109
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20170203.14
AB  - This paper systematically describes the definition, model structure, parameter estimation and corpus selection of the conditional random field model, and applies the conditional random field to the Chinese word segmentation and the Chinese word segmentation method. In this paper, a large number of experiments have been carried out using conditional random fields. The experimental corpus has been tested by Changjiang Daily for many years. Experiments are carried out to analyze the influence of the choice of conditional random field model parameters and the selection of Chinese character annotation sets on the experimental results. Furthermore, the condition of random field model can be used to add the advantages of arbitrary features, and some new features are added to the model. Word probability, the paper explores the probability characteristic of word location. Experiments on the corpus show that the introduction of the word position probability feature has improved the accuracy, recall and the value of Fl.
VL  - 2
IS  - 3
ER  -

Copy | Download