A Method of Building Chinese Field Association Knowledge from Wikipedia

被引：0

作者：

Wang, Li ^{[1
]}

Yata, Susumu ^{[1
]}

Atlam, El-sayed ^{[1
]}

Fuketa, Masao ^{[1
]}

Morita, Kazuhiro ^{[1
]}

Bando, Hiroaki ^{[1
]}

Aoe, Jun-ichi ^{[1
]}

机构：

[1] Univ Tokushima, Fac Engn, Dept Informat Sci & Intelligent Syst, Tokushima 7708506, Japan

来源：

IEEE NLP-KE 2009: PROCEEDINGS OF INTERNATIONAL CONFERENCE ON NATURAL LANGUAGE PROCESSING AND KNOWLEDGE ENGINEERING | 2009年

关键词：

Field association terms; Feature fields; Wikipedia; Chinese documents; Field recognition;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Field Association (FA) terms form a limited set of discriminating terms that give us the knowledge to identify document fields. The primary goal of this research is to make a system that can imitate the process whereby humans recognize the fields by looking at a few Chinese FA terms in a document. This paper proposes a new approach to build a Chinese FA terms dictionary automatically from Wikipedia. 104,532 FA terms are added in the dictionary. The resulting FA terms by using this dictionary are applied to recognize the fields of 5,841 documents. The average accuracy in the experiment is 92.04%. The results show that the presented method is effective in building FA terms from Wikipedia automatically.

引用

页码：568 / 572

页数：5

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