A Revised BMM and RMM Algorithm of Chinese Automatic Words Segmentation

Hui Yan Qu; Wei Zhao

doi:10.4028/www.scientific.net/AMR.267.199

Paper Titles

A Study on Pen-Based Input Operation and Tilt Angle of Tablet
p.179

Study on Security Management-Oriented Business Process Model
p.183

Research into Modeling Methods Basing on Product Presentation Information Base
p.189

The Application of Requirement Engineering Model in Large Software Development Process
p.193

A Revised BMM and RMM Algorithm of Chinese Automatic Words Segmentation
p.199

A Convergent Algorithm for Generalized Linear Complementarity Problem in Engineering Modeling
p.205

Train Optimal Control Strategy on Continuous Change Gradient Steep Downgrades
p.211

Developing of Three Degree of Freedoms SCARA Robot
p.217

A Character Experiential Learning System: An Animated Vignette Creating Tool
p.221

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 267A Revised BMM and RMM Algorithm of Chinese...

A Revised BMM and RMM Algorithm of Chinese Automatic Words Segmentation

Abstract:

The principle of Maximum Matching Method (MM) is “First Matching the Maximum Word-Length”. At present, however, the method of Maximum Matching Method (MM) does not incarnate the principle of “First Matching the Maximum Word-Length” well. So in order to incarnate well, a revised BMM and RMM Algorithm of Chinese automatic words segmentation is put forward, and its algorithm is also given.

You might also be interested in these eBooks

Manufacturing Systems and Industry Application

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 267)

Pages:

199-204

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.267.199

Citation:

Cite this paper

Online since:

June 2011

Authors:

Hui Yan Qu, Wei Zhao

Keywords:

Bound Maximum Matching Method, First Matching the Maximum Word-Length, Maximum Matching Method, Reverse Maximum Matching Method

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Yi Feng, Lin Yaping. The research present situation and development tendency of Chinese automatic words segmentation technology. Software Word, 1996 (in Chinese).

Google Scholar

[2] Zhang Chunxia, Hao Tianyong. The research present situation and difficulty of Chinese automatic words segmentation. Journal of System Simulation, 2005 (in Chinese).

Google Scholar

[3] Jie Chunyu, et al. Discuss Chinese automatic words segmentation. Journal of Chinese Information, 1989 (in Chinese).

Google Scholar

[4] Xu Hui, et al. Written Chinese automatic words segmentation expert system realization. Journal of Chinese Information, 1991 (in Chinese).

Google Scholar

[5] Yao Tianshun, et al. Based on rule Chinese automatic words segmentation system. Journal of Chinese Information, 1990 (in Chinese).

Google Scholar

[6] Li Jiafu, Zhang Yafei. A kind of probabilistic model words segmentation system. Journal of System Simulation, 2002 (in Chinese).

Google Scholar

[7] Jin Yu, Lu Qiming, et al. Maximum probability Chinese automatic words segmentation algorithm based on the context correlation. Computer Project, 2004 (in Chinese).

Google Scholar

[8] Tan Qiong, Shi Mizhi. Different meanings processing in the words segmentation. Computer Project and Application, 2002 (in Chinese).

Google Scholar

[9] Xia Ying, Chang Xingong, et al. Chinese character text recognition of using context correlation information. Journal of Chinese Information, 10(1) (in Chinese).

Google Scholar

[10] Zhang Guoxuan, et al. Fast written Chinese automatic words segmentation system and algorithm design. Journal Of Computer Research and Development, 1991, 1 (in Chinese).

Google Scholar

[11] David Yarowsky. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104, USA.

DOI: 10.3115/981658.981684

Google Scholar

[12] David Yarowsky. Word-Sense Disambiguation Using Statistical Models of Roget's Categories Trained on Large Corpora. AT&T Bell Laboratories 600 Mountain Avenue Murrary Hill NJ, 07974.

DOI: 10.3115/992133.992140

Google Scholar

[13] Christopher D. Manning, Hinrich Schutize(write), Li Qingzhong(translate), et al. Statistics natural language processing foundation. Electronics industry publishing house, 2005 (in Chinese).

Google Scholar