Forest-based Semantic Role Labeling

Forest-based Semantic Role Labeling

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by Hao Xiong , Haitao Mi , Yang Liu , Qun Liu

BibTeX

@MISC{Xiong_forest-basedsemantic,
    author = {Hao Xiong and Haitao Mi and Yang Liu and Qun Liu},
    title = {Forest-based Semantic Role Labeling},
    year = {}
}

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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 efficiently. 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.

Citations

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