The Effect of Semi-supervised Learning on Parsing Long Distance Dependencies in German and Swedish

被引：0

作者：

Sogaard, Anders ^{[1
]}

Rishoj, Christian ^{[1
]}

机构：

[1] Univ Copenhagen, Ctr Language Technol, DK-2300 Copenhagen S, Denmark

来源：

ADVANCES IN NATURAL LANGUAGE PROCESSING | 2010年 / 6233卷

关键词：

dependency parsing; semi-supervised learning; long distance dependencies;

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：

This paper shows how the best data-driven dependency parsers available today [1] can be improved by learning from unlabeled data. We focus on German and Swedish and show that labeled attachment scores improve by 1.5%-2.5%. Error analysis shows that improvements are primarily due to better recovery of long distance dependencies.

引用

页码：406 / 417

页数：12

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