A multi-agent strategy for Chinese text chunking

Ying Hong Liang; Tie Jun Zhao; Lei Mao

A multi-agent strategy for Chinese text chunking

Ying Hong Liang^*
, Tie Jun Zhao
, Lei Mao

^*Corresponding author for this work

Faculty of Computing

Harbin Institute of Technology
Northeast Forestry University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Traditional Chinese text chunking approach is to identify phrases using only one model and same features. It is shown that one model couldn't comprise each phrase's characteristics, and same features are not suitable to all phrases, data sparseness also appears. Multi-agent strategy uses several model and sensitive features of each phrase to identify different phrases. This paper describes the Multi-agent strategy applied in the identification of Chinese phrases whose main features are: 1) easy and quick communication between phrases; 2) avoidance of data sparseness. Through testing on Chinese Penn Treebank, F score of Chinese text chunking using Multi-agent strategy achieves to 95.82%, which is higher than the best result that has been reported.

Original language	English
Title of host publication	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages	57-61
Number of pages	5
State	Published - 2005
Event	International Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China Duration: 18 Aug 2005 → 21 Aug 2005

Publication series

Name	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Conference

Conference	International Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/Territory	China
City	Guangzhou
Period	18/08/05 → 21/08/05

Keywords

Multi-agent strategy
Sensitive features
Text chunking

Cite this

@inproceedings{90e11b2b8ff24be49e79e7074e029d20,

title = "A multi-agent strategy for Chinese text chunking",

abstract = "Traditional Chinese text chunking approach is to identify phrases using only one model and same features. It is shown that one model couldn't comprise each phrase's characteristics, and same features are not suitable to all phrases, data sparseness also appears. Multi-agent strategy uses several model and sensitive features of each phrase to identify different phrases. This paper describes the Multi-agent strategy applied in the identification of Chinese phrases whose main features are: 1) easy and quick communication between phrases; 2) avoidance of data sparseness. Through testing on Chinese Penn Treebank, F score of Chinese text chunking using Multi-agent strategy achieves to 95.82\%, which is higher than the best result that has been reported.",

keywords = "Multi-agent strategy, Sensitive features, Text chunking",

author = "Liang, \{Ying Hong\} and Zhao, \{Tie Jun\} and Lei Mao",

year = "2005",

language = "英语",

isbn = "078039092X",

series = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

pages = "57--61",

booktitle = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

note = "International Conference on Machine Learning and Cybernetics, ICMLC 2005 ; Conference date: 18-08-2005 Through 21-08-2005",

}

A multi-agent strategy for Chinese text chunking. / Liang, Ying Hong; Zhao, Tie Jun; Mao, Lei.
2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. 2005. p. 57-61 (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Liang, Ying Hong

AU - Zhao, Tie Jun

AU - Mao, Lei

PY - 2005

Y1 - 2005

N2 - Traditional Chinese text chunking approach is to identify phrases using only one model and same features. It is shown that one model couldn't comprise each phrase's characteristics, and same features are not suitable to all phrases, data sparseness also appears. Multi-agent strategy uses several model and sensitive features of each phrase to identify different phrases. This paper describes the Multi-agent strategy applied in the identification of Chinese phrases whose main features are: 1) easy and quick communication between phrases; 2) avoidance of data sparseness. Through testing on Chinese Penn Treebank, F score of Chinese text chunking using Multi-agent strategy achieves to 95.82%, which is higher than the best result that has been reported.

AB - Traditional Chinese text chunking approach is to identify phrases using only one model and same features. It is shown that one model couldn't comprise each phrase's characteristics, and same features are not suitable to all phrases, data sparseness also appears. Multi-agent strategy uses several model and sensitive features of each phrase to identify different phrases. This paper describes the Multi-agent strategy applied in the identification of Chinese phrases whose main features are: 1) easy and quick communication between phrases; 2) avoidance of data sparseness. Through testing on Chinese Penn Treebank, F score of Chinese text chunking using Multi-agent strategy achieves to 95.82%, which is higher than the best result that has been reported.

KW - Multi-agent strategy

KW - Sensitive features

KW - Text chunking

UR - https://www.scopus.com/pages/publications/28444485362

M3 - 会议稿件

AN - SCOPUS:28444485362

SN - 078039092X

SN - 9780780390928

T3 - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

SP - 57

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BT - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

T2 - International Conference on Machine Learning and Cybernetics, ICMLC 2005

Y2 - 18 August 2005 through 21 August 2005

ER -

A multi-agent strategy for Chinese text chunking

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