JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2014, Vol. 49 ›› Issue (1): 76-79.doi: 10.6040/j.issn.1671-9352.1.2013.213

• Articles • Previous Articles     Next Articles

Recognition method of Vietnamese named entity based on#br# conditional random fields

PAN Qing-qing, ZHOU Feng, YU Zheng-tao, GUO Jian-yi, XIAN Yan-tuan   

  1. School of Information Engineering and Automation, Kunming University of Science and Technology,
  • Received:2013-09-02 Online:2014-01-20 Published:2014-01-15

Abstract:

A method of named entity recognition is proposed based on conditional random fields model aimed at the language feature of Vietnamese. This method aims at the feature of word and part of speech, adopts the arithmetic of conditional random fields, selects the word and part of speech as the feature, defines the feature template, chooses the news text of Vietnamese, tags the six entity linguistic data such as place name, person name and organization, trains the Vietnamese entity recognition model which acquired. Vietnamese entity recognition experiment results prove that the entity recognition accuracy rate of this method reach 83.73%.

Key words: machine learning, feature selection, conditional random fields, Vietnamese named entity recognition

CLC Number: 

  • TP391
[1] HUANG Tian-yi, ZHU William. Cost-sensitive feature selection via manifold learning [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(3): 91-96.
[2] WAN Zhong-ying, WANG Ming-wen, ZUO Jia-li, WAN Jian-yi. Feature selection combined with the global and local information(GLFS) [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(5): 87-93.
[3] LI Zhao,SUN Zhan-,LI Xiao,LI Cheng,. Study on feature selection method based on information loss [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(11): 7-12.
[4] XIA Meng-nan, DU Yong-ping, ZUO Ben-xin. Micro-blog opinion analysis based on syntactic dependency and feature combination [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(11): 22-30.
[5] LIU Ming, ZAN Hong-ying, YUAN Hui-bin. Key sentiment sentence prediction using SVM and RNN [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(11): 68-73.
[6] ZHENG Yan, PANG Lin, BI Hui, LIU Wei, CHENG Gong. Feature selection algorithm based on sentiment topic model [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(11): 74-81.
[7] DU Rui-ying, YANG Yong, CHEN Jing, WANG Chi-heng. An efficient network traffic classification scheme based on similarity [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(09): 109-114.
[8] DONG Yuan1, XU Ya-bin1,2*, LI Zhuo1,2, LI Yan-ping1. Research on spam identification based on social computing and machine learning [J]. J4, 2013, 48(7): 72-78.
[9] YU Ran 1,2, LIU Chun-yang3*, JIN Xiao-long 1, WANG Yuan-zhuo 1, CHENG Xue-qi 1. Chinese spam microblog filtering based on the fusion of
multi-angle features
[J]. J4, 2013, 48(11): 53-58.
[10] HUANG Lin-sheng1, DENG Zhi-hong1,2, TANG Shi-wei1,2, WANG Wen-qing3, CHEN Ling3. A Chinese organization′s full name and matching abbreviation  algorithm based on edit-distance [J]. J4, 2012, 47(5): 43-48.
[11] YI Chao-qun, LI Jian-ping, ZHU Cheng-wen. A kind of feature selection based on classification accuracy of SVM [J]. J4, 2010, 45(7): 119-121.
[12] YANG Yu-Zhen, LIU Pei-Yu, SHU Zhen-Fang, QIU Ye. Research of an improved information gain methodusing distribution information of terms [J]. J4, 2009, 44(11): 48-51.
[13] YUAN Xiao-hang,DU Xiao-yong . iRIPPER: an improved rule-based text categorization algorithm [J]. J4, 2007, 42(11): 66-68 .
[14] YU Jun-ying,WANG Ming-wen,SHENG Jun . Class information feature selection method for text classification [J]. J4, 2006, 41(3): 144-148 .
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!