Documents ranking based on a hybrid language model for Chinese information retrieval

被引：2
|
作者：
Zheng, Dequan [1 ]
Yu, Feng [1 ]
Zhao, Tiejun [1 ,2 ]
Li, Sheng [2 ]
机构：
[1] Harbin Univ Commerce, Sch Comp & Informat Engn, 138,Tongda St, Harbin 150001, Peoples R China
[2] Harbin Univ Commerce, MOE MS Key Lab Natl Language Proc & Speech, 138,Tongda St, Harbin 150001, Peoples R China
来源：
2006 IEEE INTERNATIONAL CONFERENCE ON INFORMATION ACQUISITION, VOLS 1 AND 2, CONFERENCE PROCEEDINGS | 2006年
基金：
中国国家自然科学基金;
关键词：
language model; documents ranking; linguistic ontology knowledge; information retrieval;
D O I：
10.1109/ICIA.2006.306010
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
For information retrieval, users hope to acquire more relevant information from the top N ranking documents. In this paper, a hybrid Chinese language model is presented, which is defined as a combination of ontology with statistical method, to improve the precision of top N ranking documents by reordering the initial retrieval documents. The experiment with NTCIR-3 formal Chinese test collection shows the proposed method improved the precision at top N ranking documents level.
引用
收藏
页码：279 / 283
页数：5
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