Applying rough sets to feature extraction in POS tagging

Wei Jiang; Xiaolong Wang; Yi Guan; Zhiming Xu

Applying rough sets to feature extraction in POS tagging

Wei Jiang^*
, Xiaolong Wang
, Yi Guan
, Zhiming Xu

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

In order to extract the complicated contextual features in the part-of-speech tagging task, a novel approach based on rough sets is presented in this paper to collect the complex and long-distance features from the corpus effectively, and to overcome the noise and inconsistent sample problem existing in the corpus. In addition, these rough rules are added into the maximum entropy model. The experiment achieved the precision of 96.29 %, and increased the tagging precision by 0.83 % compared with the former model.

Original language	English
Pages (from-to)	996-1000
Number of pages	5
Journal	Gaojishu Tongxin/Chinese High Technology Letters
Volume	16
Issue number	10
State	Published - Oct 2006
Externally published	Yes

Keywords

Feature extraction
POS tagging
Rough sets

Cite this

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title = "Applying rough sets to feature extraction in POS tagging",

abstract = "In order to extract the complicated contextual features in the part-of-speech tagging task, a novel approach based on rough sets is presented in this paper to collect the complex and long-distance features from the corpus effectively, and to overcome the noise and inconsistent sample problem existing in the corpus. In addition, these rough rules are added into the maximum entropy model. The experiment achieved the precision of 96.29 \%, and increased the tagging precision by 0.83 \% compared with the former model.",

keywords = "Feature extraction, POS tagging, Rough sets",

author = "Wei Jiang and Xiaolong Wang and Yi Guan and Zhiming Xu",

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T1 - Applying rough sets to feature extraction in POS tagging

AU - Jiang, Wei

AU - Wang, Xiaolong

AU - Guan, Yi

AU - Xu, Zhiming

PY - 2006/10

Y1 - 2006/10

N2 - In order to extract the complicated contextual features in the part-of-speech tagging task, a novel approach based on rough sets is presented in this paper to collect the complex and long-distance features from the corpus effectively, and to overcome the noise and inconsistent sample problem existing in the corpus. In addition, these rough rules are added into the maximum entropy model. The experiment achieved the precision of 96.29 %, and increased the tagging precision by 0.83 % compared with the former model.

AB - In order to extract the complicated contextual features in the part-of-speech tagging task, a novel approach based on rough sets is presented in this paper to collect the complex and long-distance features from the corpus effectively, and to overcome the noise and inconsistent sample problem existing in the corpus. In addition, these rough rules are added into the maximum entropy model. The experiment achieved the precision of 96.29 %, and increased the tagging precision by 0.83 % compared with the former model.

KW - Feature extraction

KW - POS tagging

KW - Rough sets

UR - https://www.scopus.com/pages/publications/33845802092

M3 - 文章

AN - SCOPUS:33845802092

SN - 1002-0470

VL - 16

SP - 996

EP - 1000

JO - Gaojishu Tongxin/Chinese High Technology Letters

JF - Gaojishu Tongxin/Chinese High Technology Letters

IS - 10

ER -

Applying rough sets to feature extraction in POS tagging

Abstract

Keywords

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