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Improve Chinese Semantic Dependency Parsing via Syntactic Dependency Parsing

Authors: Meishan Zhang; Wanxiang Che; Yanqiu Shao; Ting Liu 0001;

Improve Chinese Semantic Dependency Parsing via Syntactic Dependency Parsing

Abstract

We address the problem of Chinese semantic dependency parsing. Dependency parsing is traditionally oriented to syntax analysis, which we denote by syntactic dependency parsing to distinguish it from semantic dependency parsing. In this paper, firstly we compare Chinese semantic dependency parsing and syntactic dependency parsing systematically, showing that syntactic dependency parsing can potentially improve the performance of semantic dependency parsing. Thus then we suggest an approach based on quasi-synchronous grammar to incorporate the auto-parsed syntactic dependency tree into semantic dependency parsing. We conduct experiments on the Chinese semantic dependency parsing corpus of SemEval-2012. Finally we achieve 65.25% LAS on test corpus, gaining increases of 2.45% compared to the top result of 62.80% in SemEval-2012.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
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