AN IMPROVED MODEL OF MST FOR CHINESE DEPENDENCY PARSING

被引：0
|
作者：
Zhang, Guiping [1 ]
Wang, Yan [1 ]
Ji, Duo [1 ]
机构：
[1] Shen Yang Aerosp Univ, Res Ctr Knowledge Engn, Shenyang 110136, Peoples R China
来源：
2012 IEEE 2ND INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND INTELLIGENT SYSTEMS (CCIS) VOLS 1-3 | 2012年
关键词：
Dependency parsing; Maximum spanning tree; Condition random fields;
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：
In this paper, a Chinese dependency parsing method is proposed based on improved Maximum Spanning Tree (MST) Parser. Within this method, dependency direction discrimination model and head POS recognition model are used to modify the weights of directed edges in the MST model, and then the Eisner algorithm is used to search and generate the dependency trees. In this paper, the problems of dependency direction discrimination and head POS recognition are converted into sequence labeling; and the modeling is done by condition random fields. We tested our method on CoNLL 2009 Share Task, and the Unlabeled Attachment Score reached 86.27%.
引用
收藏
页码：1454 / 1458
页数：5
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