Forest-Based Semantic Role Labeling

被引：0

作者：

Xiong, Hao ^{[1
]}

Mi, Haitao ^{[1
]}

Liu, Yang ^{[1
]}

Liu, Qun ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, POB 2704, Beijing 100190, Peoples R China

来源：

PROCEEDINGS OF THE TWENTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-10) | 2010年

基金：

中国国家自然科学基金;

关键词：

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：

Parsing plays an important role in semantic role labeling (SRL) because most SRL systems infer semantic relations from 1-best parses. Therefore, parsing errors inevitably lead to labeling mistakes. To alleviate this problem, we propose to use packed forest, which compactly encodes all parses for a sentence. We design an algorithm to exploit exponentially many parses to learn semantic relations efficiently. Experimental results on the CoNLL-2005 shared task show that using forests achieves an absolute improvement of 1.2% in terms of F-1 score over using 1-best parses and 0.6% over using 50-best parses.

引用

页码：1039 / 1044

页数：6

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