Forest-Based Semantic Role Labeling
Abstract
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 efciently. Experimental results on the CoNLL-2005 shared task show that using forests achieves an absolute improvement of 1.2% in terms of F1 score over using 1-best parses and 0.6% over using 50-best parses.
Cite
Text
Xiong et al. "Forest-Based Semantic Role Labeling." AAAI Conference on Artificial Intelligence, 2010. doi:10.1609/AAAI.V24I1.7716Markdown
[Xiong et al. "Forest-Based Semantic Role Labeling." AAAI Conference on Artificial Intelligence, 2010.](https://mlanthology.org/aaai/2010/xiong2010aaai-forest/) doi:10.1609/AAAI.V24I1.7716BibTeX
@inproceedings{xiong2010aaai-forest,
title = {{Forest-Based Semantic Role Labeling}},
author = {Xiong, Hao and Mi, Haitao and Liu, Yang and Liu, Qun},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2010},
pages = {1039-1044},
doi = {10.1609/AAAI.V24I1.7716},
url = {https://mlanthology.org/aaai/2010/xiong2010aaai-forest/}
}